Code for figures in ‘Large pool of organic ligands supports iron bioavailability and reduces copper toxicity in whale excrement’ by Monreal et al.
Load in libraries
library(openxlsx, quietly = TRUE)
library(tidyverse, quietly = TRUE)
library(pals, quietly = TRUE)
library(ggpattern, quietly = TRUE)
library(ggbreak, quietly = TRUE)
library(ggpattern, quietly = TRUE)
library(ggpp, quietly = TRUE)
library(ggpmisc, quietly = TRUE)
library(grid, quietly = TRUE)
library(gridExtra, quietly = TRUE)
library(cowplot, quietly = TRUE)
library(ggtext, quietly = TRUE)
library(gtable, quietly = TRUE)
library(xcms, quietly = TRUE)
library(RaMS, quietly = TRUE)
library(data.table, quietly = TRUE)
library(baseline, quietly = TRUE)
library(signal, quietly = TRUE)
library(conflicted, quietly = TRUE)
library(ggpubr, quietly = TRUE)
library(scales, quietly = TRUE)
library(RColorBrewer, quietly = TRUE)
# Use only the dplyr filter function and not the signal one
conflict_prefer("filter", "dplyr")
conflict_prefer("rename", "dplyr")
conflict_prefer("select", "dplyr")
conflict_prefer("as.data.frame", "base")
conflict_prefer("which.max","base")
conflict_prefer("unique","base")
# Path to excel sheet with all required data (except for ESI-MS .mzxml)
github_data = "~/GitHub/Whale_Manuscript/GitHub_Data_For_Whale_Ligand_Figures.xlsx"Data for Fig. 1 and Fig. 4
#######################################################
# Loading in necessary data for Fig. 1A-D
compare = read.xlsx(github_data, sheet = 1)
compare %>% as_tibble() %>% print(n=15)## # A tibble: 95 × 9
## species_samp element value_mg_kg value_umol_kg n stddev_mg_kg
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Blue Bulk Fe 162. 2897. 15 106.
## 2 Fin Bulk Fe 237. 4251. 2 45.3
## 3 Humpback Bulk Fe 119. 2124. 2 30.1
## 4 Pygmy Blue Bulk Fe 63.3 1134. 7 17
## 5 Sperm Bulk Fe 757. 13550. 1 NA
## 6 Average Bulk Fe 146. 2467. 27 135.
## 7 Blue Bulk Cu 240. 3769. 15 68.6
## 8 Fin Bulk Cu 291. 4575. 2 11.4
## 9 Humpback Bulk Cu 74.1 1166. 2 5.2
## 10 Pygmy Blue Bulk Cu 312. 4913. 7 98.6
## 11 Sperm Bulk Cu 1635. 25736. 1 NA
## 12 Average Bulk Cu 292. 3939. 27 238.
## 13 Blue Bulk P 98000 3163968. 15 19000
## 14 Fin Bulk P 121000 3906532. 2 4000
## 15 Humpback Bulk P 29000 936276. 2 21000
## # ℹ 80 more rows
## # ℹ 3 more variables: stddev_umol_kg <dbl>, sample.type <chr>, source <chr>#######################################################
#######################################################
# Loading in necessary data for Fig. 1E
tab1 = as.data.frame(read.xlsx(github_data, sheet = 2, startRow = 2))
tab1tab2 = as.data.frame(read.xlsx(github_data, sheet = 3, startRow = 3))
tab2#######################################################Data for Fig. 2, with ICPMS data used for Fig. S1 and ESIMS data used for Fig. S4 and Fig. S7 as well
#######################################################
# Loading in necessary data for Fig. 2
plot_peaks = as.data.frame(read.xlsx(github_data, sheet = 5, startRow = 1))
plot_peaks %>% as.tibble() %>% print(n = 15)## # A tibble: 70 × 9
## Peak LC_ICP_MS_Retention_Time Ferrioxamine_E-based_…¹ LC_ESI_MS_Retention_…²
## <chr> <dbl> <dbl> <dbl>
## 1 a 14.7 57.5 NA
## 2 b 15.5 31.1 NA
## 3 c 17.3 27.1 NA
## 4 c 17.3 27.1 17.6
## 5 c 17.3 27.1 17.4
## 6 d 18.1 59.6 NA
## 7 d 18.1 59.6 17.9
## 8 e 20.3 23.3 NA
## 9 e 20.3 23.3 20.3
## 10 e 20.3 23.3 20.3
## 11 f 21.0 23.8 NA
## 12 f 21.0 23.8 21.2
## 13 f 21.0 23.8 21.4
## 14 g 22.9 13.0 NA
## 15 g 22.9 13.0 22.9
## # ℹ 55 more rows
## # ℹ abbreviated names: ¹`Ferrioxamine_E-based_Concentration`,
## # ²LC_ESI_MS_Retention_Time
## # ℹ 5 more variables: FT_ICR_MS_mz <dbl>,
## # Predicted_Molecular_Ion_Formula <chr>, Assignment_Error <chr>,
## # LC_ESI_MS_Peak_Area <dbl>, Present_in_MN20 <chr>#######################################################
#######################################################
# LC-ICPMS data for Fig. 2
ICPdata = read.xlsx(github_data, sheet = 4, startRow = 1) %>%
filter(sampleID == "Whale_MN_19") %>%
select(-sampleID, -samplerun)
ICPdata$scan = as.numeric(ICPdata$scan)
ICPdata$intensity = as.numeric(ICPdata$intensity)
ICPdata$time = as.numeric(ICPdata$time)
ICPdata %>% as.tibble() %>% print(n = 15)## # A tibble: 11,337 × 4
## scan element intensity time
## <dbl> <chr> <dbl> <dbl>
## 1 0 56Fe 2520. 0.292
## 2 1 56Fe 3060. 1.10
## 3 2 56Fe 2820. 1.90
## 4 3 56Fe 2440. 2.71
## 5 4 56Fe 2540. 3.51
## 6 5 56Fe 2540. 4.32
## 7 6 56Fe 2620. 5.12
## 8 7 56Fe 2920. 5.93
## 9 8 56Fe 2920. 6.73
## 10 9 56Fe 2560. 7.54
## 11 10 56Fe 2880. 8.34
## 12 11 56Fe 2520. 9.15
## 13 12 56Fe 2220. 9.95
## 14 13 56Fe 2820. 10.8
## 15 14 56Fe 2760. 11.6
## # ℹ 11,322 more rows#######################################################
#######################################################
# LC-ESI-MS data for Fig. 2, loaded in two different data structures
timerange = c(60, 2400)
ESIrams = grabMSdata(c("~/GitHub/Whale_Manuscript/221116_Whale_MN_19.mzXML"),
grab_what=c("MS1", "MS2", "BPC"),
verbosity = "minimal",
rtrange = timerange/60) #sample file, loading in using RaMS instead of xcms##
## Reading file 221116_Whale_MN_19.mzXML... 0.36 secs
## Reading MS1 data...2.99 secs
## Reading MS2 data...0.29 secs
## Reading BPC...0.07 secs
## Total time: 3.81 secsprint(ESIrams$MS1)## rt mz int filename
## 1: 1.000662 201.0910 2509.234 221116_Whale_MN_19.mzXML
## 2: 1.000662 201.1122 3339.306 221116_Whale_MN_19.mzXML
## 3: 1.000662 201.1275 2295.875 221116_Whale_MN_19.mzXML
## 4: 1.000662 201.4804 1506.627 221116_Whale_MN_19.mzXML
## 5: 1.000662 202.0709 2307.145 221116_Whale_MN_19.mzXML
## ---
## 5844164: 39.995500 1741.8601 54162.383 221116_Whale_MN_19.mzXML
## 5844165: 39.995500 1741.9308 64592.340 221116_Whale_MN_19.mzXML
## 5844166: 39.995500 1845.5435 66742.258 221116_Whale_MN_19.mzXML
## 5844167: 39.995500 1878.8328 107937.078 221116_Whale_MN_19.mzXML
## 5844168: 39.995500 1892.8773 76211.039 221116_Whale_MN_19.mzXMLmzxcms = xcmsRaw("~/GitHub/Whale_Manuscript/221116_Whale_MN_19.mzXML",profstep=0.02,profmethod="bin",profparam=list(),includeMSn=FALSE,mslevel=NULL, scanrange=NULL)
print(mzxcms)## An "xcmsRaw" object with 7665 mass spectra
##
## Time range: 0.2-2999.6 seconds (0-50 minutes)
## Mass range: 199.9999-1999.9882 m/z
## Intensity range: 1157.87-592144000
##
## MSn data on 0 mass(es)
## with 0 MSn spectra
## Profile method: bin
## Profile step: 0.02 m/z (90000 grid points from 200 to 1999.98 m/z)
##
## Memory usage: 5370 MB#######################################################Data for Fig. 3
#######################################################
# Molecular formula data for Fig. 3 (VK plot)
cu_ligands = read.xlsx(github_data, sheet = 6) %>%
rename("mz" = "m/z",
"MF" = "[M]+",
"int" = "Intensity",
"resolution" = "Resolution",
"noise" = "Noise",
"signal_to_noise" = "S/N",
"resolution_ppm" = "dm.(ppm)",
"delta_cu_iso_err_mDa" = "Iso.Error.(mDa)",
"H_C_ratio" = "H/C",
"O_C_ratio" = "O/C",
"N_C_ratio" = "N/C",
"mz_freestyle" = "Peak.Mass",
"assignment_err_ppm_freestyle" = "Delta",
"formula_freestyle" = "Display.Formula")
cu_ligandsthis_study = cu_ligands[complete.cases(cu_ligands$mz_freestyle), ]
known_molecules = cu_ligands[c(56:nrow(cu_ligands)),]
all_with_formula = cu_ligands[complete.cases(cu_ligands$C), ]
#######################################################Data for Fig. S2
#######################################################
MN19 = as.data.frame(read.xlsx(github_data, sheet = 7, startRow = 1)) %>%
rename("potential" = "X_Values",
"0_Fe_1" = "4",
"0_Fe_2" = "5",
"1_Fe" = "6",
"2_Fe" = "7",
"3_Fe" = "8",
"5_Fe" = "9",
"7_5_Fe" = "10",
"10_Fe" = "11",
"15_Fe" = "12",
"20_Fe" = "13",
"30_Fe" = "14",
"40_Fe" = "15",
"50_Fe" = "16",
"75_Fe" = "17",
"100_Fe" = "18",
"SPIKE" = "19")
MN19 <- gather(MN19, key = "treatment", value = "Y", -potential)
MN20 = as.data.frame(read.xlsx(github_data, sheet = 8, startRow = 1)) %>%
rename("potential" = "X_Values",
"0_Fe_1" = "3",
"0_Fe_2" = "4",
"0_5_Fe" = "5",
"1_5_Fe" = "6",
"2_Fe" = "7",
"3_Fe" = "8",
"4_Fe" = "9",
"5_Fe" = "10",
"7_5_Fe" = "11",
"10_Fe" = "12",
"15_Fe" = "13",
"20_Fe" = "14",
"30_Fe" = "15",
"40_Fe" = "16",
"50_Fe" = "17",
"SPIKE" = "18")
MN20 <- gather(MN20, key = "treatment", value = "Y", -potential)
MSS1 = as.data.frame(read.xlsx(github_data, sheet = 9, startRow = 1)) %>%
rename("potential" = "X_Values",
"0_Fe_1" = "3",
"0_Fe_2" = "4",
"1_Fe" = "5",
"2_Fe" = "6",
"3_Fe" = "7",
"5_Fe" = "8",
"7_5_Fe" = "9",
"10_Fe" = "10",
"15_Fe" = "11",
"20_Fe" = "12",
"30_Fe" = "13",
"40_Fe" = "14",
"50_Fe" = "15",
"75_Fe" = "16",
"100_Fe" = "17",
"SPIKE" = "18")
MSS1 <- gather(MSS1, key = "treatment", value = "Y", -potential)
CRC7 = as.data.frame(read.xlsx(github_data, sheet = 10, startRow = 1)) %>%
rename("potential" = "X_Values",
"0_Fe_1" = "3",
"0_Fe_2" = "4",
"0_5_Fe" = "5",
"1_5_Fe" = "6",
"2_Fe" = "7",
"3_Fe" = "8",
"4_Fe" = "9",
"5_Fe" = "10",
"7_5_Fe" = "11",
"10_Fe" = "12",
"15_Fe" = "13",
"20_Fe" = "14",
"30_Fe" = "15",
"40_Fe" = "16",
"50_Fe" = "17",
"SPIKE" = "18")
CRC7 <- gather(CRC7, key = "treatment", value = "Y", -potential)
CRC210 = as.data.frame(read.xlsx(github_data, sheet = 11, startRow = 1)) %>%
rename("potential" = "X_Values",
"0_Fe_1" = "4",
"0_Fe_2" = "5",
"1_Fe" = "6",
"2_Fe" = "7",
"3_Fe" = "8",
"5_Fe" = "9",
"7_5_Fe" = "10",
"10_Fe" = "11",
"15_Fe" = "12",
"20_Fe" = "13",
"30_Fe" = "14",
"40_Fe" = "15",
"50_Fe" = "16",
"75_Fe" = "17",
"100_Fe" = "18",
"SPIKE" = "19")
CRC210 <- gather(CRC210, key = "treatment", value = "Y", -potential)
MN19$treatment = factor(MN19$treatment, levels = c("0_Fe_1",
"0_Fe_2",
"1_Fe",
"2_Fe",
"3_Fe",
"5_Fe",
"7_5_Fe",
"10_Fe",
"15_Fe",
"20_Fe",
"30_Fe",
"40_Fe",
"50_Fe",
"75_Fe",
"100_Fe",
"SPIKE"))
MN20$treatment = factor(MN20$treatment, levels = c("0_Fe_1",
"0_Fe_2",
"0_5_Fe",
"1_5_Fe",
"2_Fe",
"3_Fe",
"4_Fe",
"5_Fe",
"7_5_Fe",
"10_Fe",
"15_Fe",
"20_Fe",
"30_Fe",
"40_Fe",
"50_Fe",
"SPIKE"))
MSS1$treatment = factor(MSS1$treatment, levels = c("0_Fe_1",
"0_Fe_2",
"1_Fe",
"2_Fe",
"3_Fe",
"5_Fe",
"7_5_Fe",
"10_Fe",
"15_Fe",
"20_Fe",
"30_Fe",
"40_Fe",
"50_Fe",
"75_Fe",
"100_Fe",
"SPIKE"))
CRC7$treatment = factor(CRC7$treatment, levels = c("0_Fe_1",
"0_Fe_2",
"0_5_Fe",
"1_5_Fe",
"2_Fe",
"3_Fe",
"4_Fe",
"5_Fe",
"7_5_Fe",
"10_Fe",
"15_Fe",
"20_Fe",
"30_Fe",
"40_Fe",
"50_Fe",
"SPIKE"))
CRC210$treatment = factor(CRC210$treatment, levels = c("0_Fe_1",
"0_Fe_2",
"1_Fe",
"2_Fe",
"3_Fe",
"5_Fe",
"7_5_Fe",
"10_Fe",
"15_Fe",
"20_Fe",
"30_Fe",
"40_Fe",
"50_Fe",
"75_Fe",
"100_Fe",
"SPIKE"))
MN19MN20MSS1CRC7CRC210#######################################################Data for Fig. S3
#######################################################
MN19_eHS = as.data.frame(read.xlsx(github_data, sheet = 14, startRow = 1)) %>%
rename("potential" = "X_Values",
"no_SRHA_added_1" = "2",
"no_SRHA_added_2" = "3",
"no_SRHA_added_3" = "4",
"25_SRHA_added" = "5",
"50_SRHA_added" = "6",
"100_SRHA_added" = "7",
"150_SRHA_added" = "8",
"300_SRHA_added" = "9")
MN19_eHS <- gather(MN19_eHS, key = "treatment", value = "Y", -potential)
MN20_eHS = as.data.frame(read.xlsx(github_data, sheet = 15, startRow = 1)) %>%
rename("potential" = "X_Values",
"no_SRHA_added_1" = "2",
"no_SRHA_added_2" = "3",
"no_SRHA_added_3" = "4",
"25_SRHA_added" = "5",
"50_SRHA_added" = "6",
"100_SRHA_added" = "7",
"150_SRHA_added" = "8",
"300_SRHA_added" = "9")
MN20_eHS <- gather(MN20_eHS, key = "treatment", value = "Y", -potential)
MSS1_eHS = as.data.frame(read.xlsx(github_data, sheet = 16, startRow = 1)) %>%
rename("potential" = "X_Values",
"no_SRHA_added_1" = "2",
"no_SRHA_added_2" = "3",
"no_SRHA_added_3" = "4",
"25_SRHA_added" = "5",
"50_SRHA_added" = "6",
"100_SRHA_added" = "7",
"150_SRHA_added" = "8",
"225_SRHA_added" = "9")
MSS1_eHS <- gather(MSS1_eHS, key = "treatment", value = "Y", -potential)
CRC7_eHS = as.data.frame(read.xlsx(github_data, sheet = 17, startRow = 1)) %>%
rename("potential" = "X_Values",
"no_SRHA_added_1" = "2",
"no_SRHA_added_2" = "3",
"no_SRHA_added_3" = "4",
"25_SRHA_added" = "5",
"50_SRHA_added" = "6",
"100_SRHA_added" = "7",
"150_SRHA_added" = "8",
"225_SRHA_added" = "9")
CRC7_eHS <- gather(CRC7_eHS, key = "treatment", value = "Y", -potential)
CRC210_eHS = as.data.frame(read.xlsx(github_data, sheet = 18, startRow = 1)) %>%
rename("potential" = "X_Values",
"no_SRHA_added_1" = "2",
"no_SRHA_added_2" = "3",
"no_SRHA_added_3" = "4",
"25_SRHA_added" = "5",
"50_SRHA_added" = "6",
"100_SRHA_added" = "7",
"150_SRHA_added" = "8",
"225_SRHA_added" = "9")
CRC210_eHS <- gather(CRC210_eHS, key = "treatment", value = "Y", -potential)
MN19_eHS$treatment = factor(MN19_eHS$treatment, levels = c("no_SRHA_added_1",
"no_SRHA_added_2",
"no_SRHA_added_3",
"25_SRHA_added",
"50_SRHA_added",
"100_SRHA_added",
"150_SRHA_added",
"300_SRHA_added"))
MN20_eHS$treatment = factor(MN20_eHS$treatment, levels = c("no_SRHA_added_1",
"no_SRHA_added_2",
"no_SRHA_added_3",
"25_SRHA_added",
"50_SRHA_added",
"100_SRHA_added",
"150_SRHA_added",
"300_SRHA_added"))
MSS1_eHS$treatment = factor(MSS1_eHS$treatment, levels = c("no_SRHA_added_1",
"no_SRHA_added_2",
"no_SRHA_added_3",
"25_SRHA_added",
"50_SRHA_added",
"100_SRHA_added",
"150_SRHA_added",
"225_SRHA_added"))
CRC7_eHS$treatment = factor(CRC7_eHS$treatment, levels = c("no_SRHA_added_1",
"no_SRHA_added_2",
"no_SRHA_added_3",
"25_SRHA_added",
"50_SRHA_added",
"100_SRHA_added",
"150_SRHA_added",
"225_SRHA_added"))
CRC210_eHS$treatment = factor(CRC210_eHS$treatment, levels = c("no_SRHA_added_1",
"no_SRHA_added_2",
"no_SRHA_added_3",
"25_SRHA_added",
"50_SRHA_added",
"100_SRHA_added",
"150_SRHA_added",
"225_SRHA_added"))
MN19_eHSMN20_eHSMSS1_eHSCRC7_eHSCRC210_eHS#######################################################Data for Fig. S6
#######################################################
ESIrams_FT = grabMSdata(c("~/GitHub/Whale_Manuscript/WHALE_Mn19.mzXML"),
grab_what=c("MS1", "MS2", "BPC"),
verbosity = "minimal",
rtrange = timerange/60) #sample file, loading in using RaMS instead of xcms##
## Reading file WHALE_Mn19.mzXML... 0.05 secs
## Reading MS1 data...0.21 secs
## Reading MS2 data...0.03 secs
## Reading BPC...0.01 secs
## Total time: 0.31 secsmzxcms_FT = xcmsRaw("~/GitHub/Whale_Manuscript/WHALE_Mn19.mzXML",profstep=0.02,profmethod="bin",profparam=list(),includeMSn=FALSE,mslevel=NULL, scanrange=NULL)
# First, Align ICPMS and Orbitrap data based on cyanocobalamin
offset_FT <- 277.43681
mzxcms_FT@scantime<-mzxcms@scantime+offset_FT
ESIrams_FT$MS1$rt = ESIrams_FT$MS1$rt+(offset_FT/60)
ESIrams_FT$MS2$rt = ESIrams_FT$MS2$rt+(offset_FT/60)
ESIrams_FT$MS1ESIrams_FT$MS2#######################################################Data for Fig. S9
MN19_Cu = as.data.frame(read.xlsx(github_data, sheet = 13, startRow = 1)) %>%
rename("potential" = "X_Values",
"0_Fe_1" = "3",
"0_Fe_2" = "4",
"1_Fe" = "5",
"2_Fe" = "6",
"3_Fe" = "7",
"5_Fe" = "8",
"7_5_Fe" = "9",
"10_Fe" = "10",
"15_Fe" = "11",
"20_Fe" = "12",
"30_Fe" = "13",
"40_Fe" = "14",
"50_Fe" = "15",
"75_Fe" = "16",
"100_Fe" = "17",
"SPIKE" = "18")
MN19_Cu <- gather(MN19_Cu, key = "treatment", value = "Y", -potential)
MN20_Cu = as.data.frame(read.xlsx(github_data, sheet = 12, startRow = 1)) %>%
rename("potential" = "X_Values",
"0_Fe_1" = "5",
"0_Fe_2" = "6",
"1_Fe" = "7",
"2_Fe" = "8",
"3_Fe" = "9",
"5_Fe" = "10",
"7_5_Fe" = "11",
"10_Fe" = "12",
"15_Fe" = "13",
"20_Fe" = "14",
"30_Fe" = "15",
"40_Fe" = "16",
"50_Fe" = "17",
"75_Fe" = "18",
"100_Fe" = "19",
"SPIKE" = "20")
MN20_Cu <- gather(MN20_Cu, key = "treatment", value = "Y", -potential)
MN19_Cu$treatment = factor(MN19_Cu$treatment, levels = c("0_Fe_1",
"0_Fe_2",
"1_Fe",
"2_Fe",
"3_Fe",
"5_Fe",
"7_5_Fe",
"10_Fe",
"15_Fe",
"20_Fe",
"30_Fe",
"40_Fe",
"50_Fe",
"75_Fe",
"100_Fe",
"SPIKE"))
MN20_Cu$treatment = factor(MN20_Cu$treatment, levels = c("0_Fe_1",
"0_Fe_2",
"1_Fe",
"2_Fe",
"3_Fe",
"5_Fe",
"7_5_Fe",
"10_Fe",
"15_Fe",
"20_Fe",
"30_Fe",
"40_Fe",
"50_Fe",
"75_Fe",
"100_Fe",
"SPIKE"))
MN19_CuMN20_Cu#######################################################Data for Fig. S10
std_curve <- data.frame(
Concentration = c(0, 25, 50, 100, 200),
Peak_Area = c(46491.23368, 247200.2432, 430735.6015, 748850.5695, 1382684.257)
)
std_curvestd_icpms_data = read.xlsx(github_data, sheet = 4, startRow = 1) %>%
filter(grepl("Std|Blank", sampleID)) %>%
filter(sampleID %in% c("Std_1_25nM_1", "Std_2_50nM_2","Std_3_100nM_1","Std_4_200nM_1","Blank2")) %>%
filter(element == "56Fe")
std_icpms_data$intensity = as.numeric(std_icpms_data$intensity)
std_icpms_data$time = as.numeric(std_icpms_data$time)
# Define a mapping of sampleIDs to concentration values
concentration_mapping <- c("Std_1_25nM_1" = "25",
"Std_2_50nM_2" = "50",
"Std_3_100nM_1" = "100",
"Std_4_200nM_1" = "200",
"Blank2" = "0")
# Replace sampleIDs with concentration values
std_icpms_data$sampleID <- concentration_mapping[std_icpms_data$sampleID]
# Order sampleID as a factor in the desired order
std_icpms_data$sampleID <- factor(std_icpms_data$sampleID, levels = c("0", "25", "50", "100", "200"))
std_icpms_data#######################################################Figure 1
Figure 1A
Barplot of dFe values of fecal samples from two humpback whales around Palmer Station (MN19, MN20) and three blue whales from the California current (CRC7, CRC210, MSS1). Only other dFe values for these type of samples are also plotted from literature for comparison.
dfe_frame = compare %>% filter(element %in% c("dFe","SPE-Fe") & species_samp %in% c("MN19","MN20","CRC7","CRC210","MSS1","Ratnarajah et al. (2017) 1","Ratnarajah et al. (2017) 2","Ratnarajah et al. (2017) 3"))
dfe_frame$species_samp = factor(dfe_frame$species_samp, levels = c("MN19","MN20","CRC7","CRC210","MSS1","Ratnarajah et al. (2017) 1","Ratnarajah et al. (2017) 2","Ratnarajah et al. (2017) 3"))
ggplot(dfe_frame) +
labs(x = "",
y = "<b><span style = 'color:#00000000;'>[dFe<sub></sub>] (µ</span><span style = 'color:#CD6600;'>[dFe<sub></sub>] (µM)</span><span style = 'color:#000000;'>, </span><span style = 'color:#F2AD00;'>[Fe-L]<sub>SPE</sub> (µM)</span></b>") +
geom_col(aes(x = species_samp,
y = value_umol_kg,
fill = element),
color = "black",
position = position_dodge2(preserve = "single", padding = 0)) +
#patterned bar for SPE values to show efficiency ranges
geom_rect_pattern(xmin = 1,
xmax = 1.45,
ymin = 0.1410/0.4,
ymax = 0.1410/0.05,
fill = "gray90",
color = "black",
pattern = "stripe",
pattern_fill = "#F2AD00",
pattern_color = "#F2AD00",
pattern_spacing = 0.045) +
#patterned bar for SPE values to show efficiency ranges
geom_rect_pattern(xmin = 2,
xmax = 2.45,
ymin = 0.0273/0.4,
ymax = 0.0273/0.05,
fill = "gray90",
color = "black",
pattern = "stripe",
pattern_fill = "#F2AD00",
pattern_color = "#F2AD00",
pattern_spacing = 0.045) +
geom_text(label = "5-40% efficiency",
x = (1+1.45)/2,
y = ((0.1410/0.4)+(0.1410/0.05))/2,
angle = 90,
size = 4.3,
fontface = "bold") +
geom_text(label = "This Study",
x = 2.5,
y = 7.5,
fontface = "bold",
size = 6) +
geom_text(label = "Ratnarajah et al.\n(2017)",
x = 7,
y = 7.5,
fontface = "bold",
size = 6) +
geom_errorbar(data = dfe_frame %>% filter(species_samp %in% c("MN19","MN20")),
aes(x = species_samp,
ymin = (value_umol_kg - stddev_umol_kg),
ymax = (value_umol_kg + stddev_umol_kg)),
width = 0.125, linewidth = 0.75,
position = position_nudge(x = -0.225)) +
geom_errorbar(data = dfe_frame %>% filter(!(species_samp %in% c("MN19","MN20"))),
aes(x = species_samp,
ymin = (value_umol_kg - stddev_umol_kg),
ymax = (value_umol_kg + stddev_umol_kg)),
width = 0.125, linewidth = 0.75,
position = position_nudge(x = 0)) +
scale_y_cut(breaks = c(4), which = c(1,2), scales = c(1,2), expand = expansion(mult = c(0, .1))) +
scale_x_discrete(expand = c(0,0),
labels = c("MN19","MN20","CRC7","CRC210","MSS1","Humpback 1","Humpback 2","Humpback 3")) +
geom_vline(xintercept = 5.5, linetype = "dashed", color = "black", size = 0.75) +
#geom_rect to cover up overplotting with ggbreak
geom_rect(aes(xmin = 1, xmax = 1.5,ymin=4,ymax=7.5), fill = "white") +
scale_fill_manual(values = c("#CD6600","#F2AD00")) +
coord_cartesian(ylim = c(0,14)) +
theme_classic2(base_size = 19) +
theme(axis.title.x = element_blank(),
axis.title.y.left = element_markdown(),
legend.position = "none",
axis.text.x = element_text(angle = 45, face = "bold", hjust = 1, color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"))
Figure 1B
Barplot of ligand (L2, L4, and electroactive humic substances) values of fecal samples from two humpback whales around Palmer Station (MN19, MN20) and three blue whales from the California current (CRC7, CRC210, MSS1). Maximum dFe value from Fig. 1A is plotted as a horizontal dashed line to show how much ligand was present in the samples relative to dFe
ligand_frame = compare %>% filter(element %in% c("Ltotal","L2","L4","Cu-Ltotal","eHS") & species_samp %in% c("MN19","MN20","CRC7","CRC210","MSS1"))
ligand_frame$species_samp = factor(ligand_frame$species_samp, levels = c("MN19","MN20","CRC7","CRC210","MSS1"))
ligand_frame$element = factor(ligand_frame$element, levels = c("Ltotal","Cu-Ltotal","eHS","L2","L4"))
# GROUPED BAR GRAPH WITH LIGANDS AND HUMICS
ggplot() +
geom_col(data = ligand_frame %>% filter(!(element %in% c("L2","L4","Cu-Ltotal"))),
aes(x = species_samp,
y = value_umol_kg,
fill = element),
position = "dodge",
color = "black") +
geom_col(data = ligand_frame %>% filter(element %in% c("L2", "L4")),
aes(x = species_samp,
y = value_umol_kg,
fill = element),
#position = "stack",
position_stacknudge(x = -0.25),
color = "black",
width = 0.5) +
geom_errorbar(data = ligand_frame %>% filter(element == "eHS"),
aes(x = species_samp,
ymin = value_umol_kg - stddev_umol_kg,
ymax = value_umol_kg + stddev_umol_kg),
width = 0.125,
size = 0.75,
position = position_nudge(x = 0.225)) +
geom_errorbar(data = ligand_frame %>% filter(element == "Ltotal"),
aes(x = species_samp,
ymin = value_umol_kg - stddev_umol_kg,
ymax = value_umol_kg + stddev_umol_kg),
width = 0.125,
size = 0.75,
position = position_nudge(x = -0.25)) +
geom_hline(yintercept = 12.62,
linetype = "longdash",
size = 1,
alpha = 0.75,
color = "black") +
geom_text(aes(label = "Max\n[dFe]",
x = 1,
y = 70),
nudge_x = -0.25,
size = 4.5,
color = "black",
fontface = "bold",
alpha = 0.75) +
labs(x = "",
y = "<b><span style = 'color:#79402E;'>[L<sub>2</sub>] (µM eq Fe)</span><span style = 'color:#000000;'>, </span><span style = 'color:#AA9486;'>[L<sub>4</sub>] (µM eq Fe)</span><br><span style = 'color:#9986A5;'>[eHS]<sub></sub> (µM eq Fe)</span></b>") +
scale_fill_manual(values = c("#9986A5","#79402E","#AA9486","#000000")) +
coord_cartesian(expand = FALSE) +
scale_y_continuous(limits = c(0,1000),
breaks = c(100,200,300,400,500,600,700,800,900)) +
theme_classic2(base_size = 19) +
theme(axis.title.x = element_blank(),
axis.title.y = element_markdown(),
axis.text.x = element_text(angle = 45, face = "bold", hjust = 1, color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
axis.title.y.right = element_markdown(),
legend.position = "none",
axis.text.y.right = element_text(face = "bold", color = "black"))
Figure 1C
Barplot of dCu values of fecal samples from two humpback whales around Palmer Station (MN19, MN20) and three blue whales from the California current (CRC7, CRC210, MSS1).
dcu_frame = compare %>% filter(element %in% c("dCu","SPE-Cu") & species_samp %in% c("MN19","MN20","CRC7","CRC210","MSS1"))
dcu_frame$species_samp = factor(dcu_frame$species_samp, levels = c("MN19","MN20","CRC7","CRC210","MSS1"))
ggplot(dcu_frame) +
labs(x = "",
y = "<b><span style = 'color:#00A08A;'>[dCu<sub></sub>] (µM)</span><span style = 'color:#000000;'>, </span><span style = 'color:#D8B70A;'>[Cu-L]<sub>SPE</sub> (µM)</span></b>") +
geom_col(aes(x = species_samp,
y = value_umol_kg,
fill = element),
color = "black",
position = position_dodge2(preserve = "single", padding = 0)) +
geom_errorbar(data = dcu_frame %>% filter(element == "dCu" & !(species_samp %in% c("MN19", "MN20"))),
aes(x = species_samp,
ymin = value_umol_kg - stddev_umol_kg,
ymax = value_umol_kg + stddev_umol_kg),
width = 0.125,
size = 0.75,
position = position_nudge(x = 0)) +
geom_errorbar(data = dcu_frame %>% filter(element == "dCu" & (species_samp %in% c("MN19", "MN20"))),
aes(x = species_samp,
ymin = value_umol_kg - stddev_umol_kg,
ymax = value_umol_kg + stddev_umol_kg),
width = 0.125,
size = 0.75,
position = position_nudge(x = -0.225)) +
geom_rect_pattern(xmin = 1,
xmax = 1.45,
ymin = 4.899/0.4,
ymax = 4.899/0.05,
fill = "gray90",
color = "black",
pattern = "stripe",
pattern_fill = "#D8B70A",
pattern_color = "#D8B70A",
pattern_spacing = 0.03) +
geom_rect_pattern(xmin = 2,
xmax = 2.45,
ymin = 1.153/0.4,
ymax = 1.153/0.05,
fill = "gray90",
color = "black",
pattern = "stripe",
pattern_fill = "#D8B70A",
pattern_color = "#D8B70A",
pattern_spacing = 0.03) +
geom_text(label = "5-40% efficiency",
x = (1+1.45)/2,
y = ((4.899/0.4)+(4.899/0.05))/2,
angle = 90,
size = 4.3,
fontface = "bold") +
scale_fill_manual(values = c("#00A08A","#D8B70A")) +
scale_y_continuous(breaks = c(0,50,100,150,200,250,300),
lim = c(0,325)) +
scale_x_discrete(labels = c("MN19","MN20","CRC7","CRC210","MSS1")) +
coord_cartesian(expand = FALSE) +
theme_classic2(base_size = 19) +
theme(axis.title.x = element_blank(),
axis.title.y.left = element_markdown(),
axis.text.x = element_text(angle = 45, face = "bold", hjust = 1, color = "black"),
legend.position = "none",
axis.text.y.left = element_text(face = "bold", color = "black"))
Figure 1D
Barplot of Cu ligand values of fecal samples from two humpback whales around Palmer Station (MN19, MN20) and three blue whales from the California current (CRC7, CRC210, MSS1).
dcu_lig_frame = compare %>% filter(element %in% c("Cu-Ltotal") & species_samp %in% c("MN19","MN20"))
dcu_lig_frame$species_samp = factor(dcu_lig_frame$species_samp, levels = c("MN19","MN20"))
ggplot(dcu_lig_frame) +
labs(x = "",
y = "<b><span style = 'color:#82A78B;'>[L<sub>Cu</sub>]<sub>CSV</sub> (µM eq Cu)</span></b>") +
geom_col(aes(x = species_samp,
y = value_umol_kg,
fill = element),
color = "black",
width = 0.5,
position = position_dodge2(preserve = "single", padding = 0)) +
geom_errorbar(aes(x = species_samp,
ymin = value_umol_kg - stddev_umol_kg,
ymax = value_umol_kg + stddev_umol_kg),
width = 0.125,
size = 0.75,
position = position_nudge(x = 0)) +
scale_fill_manual(values = c("#82A78B")) +
scale_y_continuous(breaks = c(0,50,100,150,200,250,300),
lim = c(0,325)) +
scale_x_discrete(labels = c("MN19","MN20")) +
coord_cartesian(expand = FALSE) +
theme_classic2(base_size = 19) +
theme(axis.title.x = element_blank(),
axis.title.y.left = element_markdown(),
axis.text.x = element_text(angle = 45, face = "bold", hjust = 1, color = "black"),
legend.position = "none",
axis.text.y.left = element_text(face = "bold", color = "black"))
Figure 1E
Table of additional values from CLE-ACSV (binding strength represented by logK, free metal concentrations) and descriptions of samples
#Reformatting data
tab1[1,3] = "Antarctic\nPeninsula"
tab1[2,3] = "Antarctic\nPeninsula"
tab1[3,3] = "California\nCurrent"
tab1[4,3] = "California\nCurrent"
tab1[5,3] = "California\nCurrent"
tab1[1,4] = "dark brown,\nviscous"
tab1[2,4] = "light pink,\norange"
tab1[3,4] = "light pink,\nclear"
tab1[4,4] = "brown,\norange"
tab1[5,4] = "dark brown,\nhighly viscous"
tab1[1,5] = "0.2278\n± 0.167"
tab1[2,5] = "0.0962\n± 0.021"
tab1[3,5] = "0.0828\n± 0.028"
tab1[4,5] = "0.3628\n± 0.267"
tab1[5,5] = "0.518\n± 0.102"
colnames(tab1) <- c("Sample",
"Species",
"Region",
"Filtrate\nDescription",
"[Fe`] (nM)")
#Reformatting data
tab2[1,2] = "9.184\n± 0.060"
tab2[2,2] = "8.761\n± 0.057"
tab2[3,2] = "8.196\n± 0.094"
tab2[4,2] = "8.915\n± 0.067"
tab2[5,2] = "9.649\n± 0.072"
tab2[1,3] = "11.36\n± 0.284"
tab2[2,3] = "11.672\n± 0.064"
tab2[3,3] = "11.888\n± 0.101"
tab2[4,3] = "11.235\n± 0.253"
tab2[5,3] = "11.692\n± 0.058"
tab2[1,4] = "14.47\n± 0.09"
tab2[2,4] = "14.38\n± 0.04"
tab2[1,5] = "4.246\n± 1.539"
tab2[2,5] = "2.205\n± 0.321"
colnames(tab2) <- c(bquote(bold(Sample)),
bquote(bold(logK[FeL["4"]*", Fe`"]^cond)),
bquote(bold(logK[FeL["2"]*", Fe`"]^cond)),
bquote(bold(logK[CuL*", Cu`"]^cond)),
"[Cu`] (pM)")
t1 = tableGrob(tab1, rows = NULL)
t1 <- gtable_add_grob(t1,
grobs = rectGrob(gp = gpar(fill = NA, lwd = 4)),
t = 1, l = 1, r = ncol(t1))
t2 = tableGrob(tab2, theme = ttheme_default(parse = TRUE), rows = NULL)
t2 <- gtable_add_grob(t2,
grobs = rectGrob(gp = gpar(fill = NA, lwd = 4)),
t = 1, l = 1, r = ncol(t2))
valigned <- gtable_combine(t1,t2, along=2)
valigned <- gtable_add_grob(valigned,
grobs = rectGrob(gp = gpar(fill = NA, lwd = 4)),
t = 1, b = nrow(valigned), l = 1, r = ncol(valigned))
grid.arrange(valigned)
Figure 2
(A) 63Cu LC-ICP-MS chromatogram for sample MN19, along with (B) extracted ion chromatograms (EICs) of identified LC-ESI-MS (Orbitrap) peaks that correspond to ICP-MS peaks. Opaque colors represent EICs corresponding to 63Cu-bound masses while transparent colors represent EICs corresponding to 65Cu-bound masses, scaled by the natural abundance isotope ratio of 63Cu/65Cu. Exact masses are given from LC-FT-ICR-MS data at the 21 Tesla facility.
### Function to align ICPMS and ESIMS data using cyanocobalamin (internal standard) peaks
### Typically this function is more interactive, but a simplified version is here for the sake of the markdown file
timerange = c(60,2400)
multiMSalign <- function(mzxcms,ICPdata,timerange){
#Find max Co signal in ICPMS chromatogram
ICPComax<-ICPdata[which.max(ICPdata[,"intensity"]),"time"]
#Get retention time (in seconds) of Co peak (cobalamin) in Orbitrap data
B12profile<-rawEIC(mzxcms,c(678.285,678.295))
B12profile<-do.call(cbind,B12profile)
B12maxscan<-B12profile[which.max(B12profile[,2]),1]
OrbiComax<-mzxcms@scantime[B12maxscan]
#Offset between ICPMS and ESIMS
Offset<-ICPComax-OrbiComax
mzxcms<-mzxcms
return(Offset)
}
# First, ALign ICPMS and Orbitrap data based on cyanocobalamin
offset<-multiMSalign(mzxcms,ICPdata %>% filter(element == "59Co"),timerange)
# Apply Co Offest
#peaks$icprt = as.numeric(peaks$icprt) + (offset/60)
#apo_peaks$apo_icprt_real = as.numeric(apo_peaks$apo_icprt_real) + (offset/60)
mzxcms@scantime<-mzxcms@scantime+offset
ESIrams$MS1$rt = ESIrams$MS1$rt+(offset/60)
# Prepare ICPMS data for plotting
element_to_analyze = "63Cu"
ICPdat_onesamp_onelement <- ICPdata %>%
filter(element == element_to_analyze) %>%
filter(time > 300) # excluding first 5 min before LC pump switches from loading pump to nano pumps
ICPdat_onesamp_onelement.t <- t(ICPdat_onesamp_onelement$intensity) %>% as.matrix()
# Find baseline for plotting
bc.als <- baseline(ICPdat_onesamp_onelement.t,
lambda = 8, p = .01, maxit = 20, method='als')
# Applies baseline correction and smooths out ICPMS data
ICPdat_onesamp_onelement_wbl <- ICPdat_onesamp_onelement %>%
mutate(intensity_baselined = c(getBaseline(bc.als)),
intensity_corrected = c(getCorrected(bc.als))) %>%
mutate(intensity_smoothed = sgolayfilt(intensity, p = 15),
intensity_corrected_smoothed = sgolayfilt(intensity_corrected, p = 15))
# Further smoothing
ICP<-cbind(ICPdat_onesamp_onelement_wbl[,c("time")],
ICPdat_onesamp_onelement_wbl[,c("intensity_corrected_smoothed")])
colnames(ICP) = c("RT","Int")
ICP_smoothish<-ksmooth(ICP[,1],ICP[,2],kernel="normal",bandwidth=3.5)
# Convert LC_ICP_MS_Retention_Time to seconds
plot_peaks$LC_ICP_MS_Retention_Time_seconds <- plot_peaks$LC_ICP_MS_Retention_Time * 60
# Function to find the index of the closest value in ICPMS data
find_closest_index <- function(value, vec) {
idx <- which.min(abs(vec - value))
return(idx)
}
# Find the index of the closest value for each LC_ICP_MS_Retention_Time
index <- sapply(plot_peaks$LC_ICP_MS_Retention_Time_seconds, function(rt) {
find_closest_index(rt, ICP_smoothish$x)
})
# Add the ICP_max_int column to plot_peaks
plot_peaks$ICP_max_int <- ICP_smoothish$y[index]
# Starting to subset data by peaks for Fig. 2B
# only plotting peaks c through o
subset_peaks_c = (plot_peaks %>% filter(Peak == "c"))[-1,]
subset_peaks_d = (plot_peaks %>% filter(Peak == "d"))[-1,]
subset_peaks_e = (plot_peaks %>% filter(Peak == "e"))[-1,]
subset_peaks_f = (plot_peaks %>% filter(Peak == "f"))[-1,]
subset_peaks_g = (plot_peaks %>% filter(Peak == "g"))[-1,]
subset_peaks_h = (plot_peaks %>% filter(Peak == "h"))[-1,]
subset_peaks_i = (plot_peaks %>% filter(Peak == "i"))[-1,]
subset_peaks_j = (plot_peaks %>% filter(Peak == "j"))[-1,]
subset_peaks_k = (plot_peaks %>% filter(Peak == "k"))[-1,]
subset_peaks_l = (plot_peaks %>% filter(Peak == "l"))[-1,]
subset_peaks_m = (plot_peaks %>% filter(Peak == "m"))[-1,]
subset_peaks_n = (plot_peaks %>% filter(Peak == "n"))[-1,]
subset_peaks_o = (plot_peaks %>% filter(Peak == "o"))[-1,]
subset_peaks_c_63Cu = vector("list", nrow(subset_peaks_c))
subset_peaks_c_65Cu = vector("list", nrow(subset_peaks_c))
subset_peaks_d_63Cu = vector("list", nrow(subset_peaks_d))
subset_peaks_d_65Cu = vector("list", nrow(subset_peaks_d))
subset_peaks_e_63Cu = vector("list", nrow(subset_peaks_e))
subset_peaks_e_65Cu = vector("list", nrow(subset_peaks_e))
subset_peaks_f_63Cu = vector("list", nrow(subset_peaks_f))
subset_peaks_f_65Cu = vector("list", nrow(subset_peaks_f))
subset_peaks_g_63Cu = vector("list", nrow(subset_peaks_g))
subset_peaks_g_65Cu = vector("list", nrow(subset_peaks_g))
subset_peaks_h_63Cu = vector("list", nrow(subset_peaks_h))
subset_peaks_h_65Cu = vector("list", nrow(subset_peaks_h))
subset_peaks_i_63Cu = vector("list", nrow(subset_peaks_i))
subset_peaks_i_65Cu = vector("list", nrow(subset_peaks_i))
subset_peaks_j_63Cu = vector("list", nrow(subset_peaks_j))
subset_peaks_j_65Cu = vector("list", nrow(subset_peaks_j))
subset_peaks_k_63Cu = vector("list", nrow(subset_peaks_k))
subset_peaks_k_65Cu = vector("list", nrow(subset_peaks_k))
subset_peaks_l_63Cu = vector("list", nrow(subset_peaks_l))
subset_peaks_l_65Cu = vector("list", nrow(subset_peaks_l))
subset_peaks_m_63Cu = vector("list", nrow(subset_peaks_m))
subset_peaks_m_65Cu = vector("list", nrow(subset_peaks_m))
subset_peaks_n_63Cu = vector("list", nrow(subset_peaks_n))
subset_peaks_n_65Cu = vector("list", nrow(subset_peaks_n))
subset_peaks_o_63Cu = vector("list", nrow(subset_peaks_o))
subset_peaks_o_65Cu = vector("list", nrow(subset_peaks_o))
# Goes through each LCICPMS peak, and subsets a retention time window for the EICs
for(i in 1:nrow(subset_peaks_c)){
pktime = range(subset_peaks_c$LC_ESI_MS_Retention_Time)
pktime[1] = pktime[1] - 1.5
pktime[2] = pktime[2] + 1.5
moi = subset_peaks_c$FT_ICR_MS_mz[i]
ESI_rt_window = ESIrams$MS1[rt%between%(pktime)]
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_c_63Cu[[i]] = ms1
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_c_65Cu[[i]] = ms1
}
for(i in 1:nrow(subset_peaks_d)){
pktime = range(subset_peaks_d$LC_ESI_MS_Retention_Time)
pktime[1] = pktime[1] - 1.5
pktime[2] = pktime[2] + 1.5
moi = subset_peaks_d$FT_ICR_MS_mz[i]
ESI_rt_window = ESIrams$MS1[rt%between%(pktime)]
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_d_63Cu[[i]] = ms1
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_d_65Cu[[i]] = ms1
}
for(i in 1:nrow(subset_peaks_e)){
pktime = range(subset_peaks_e$LC_ESI_MS_Retention_Time)
pktime[1] = pktime[1] - 1.5
pktime[2] = pktime[2] + 1.5
moi = subset_peaks_e$FT_ICR_MS_mz[i]
ESI_rt_window = ESIrams$MS1[rt%between%(pktime)]
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_e_63Cu[[i]] = ms1
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_e_65Cu[[i]] = ms1
}
for(i in 1:nrow(subset_peaks_f)){
pktime = range(subset_peaks_f$LC_ESI_MS_Retention_Time)
pktime[1] = pktime[1] - 1.5
pktime[2] = pktime[2] + 1.5
moi = subset_peaks_f$FT_ICR_MS_mz[i]
ESI_rt_window = ESIrams$MS1[rt%between%(pktime)]
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_f_63Cu[[i]] = ms1
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_f_65Cu[[i]] = ms1
}
for(i in 1:nrow(subset_peaks_g)){
pktime = range(subset_peaks_g$LC_ESI_MS_Retention_Time)
pktime[1] = pktime[1] - 1.5
pktime[2] = pktime[2] + 1.5
moi = subset_peaks_g$FT_ICR_MS_mz[i]
ESI_rt_window = ESIrams$MS1[rt%between%(pktime)]
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_g_63Cu[[i]] = ms1
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_g_65Cu[[i]] = ms1
}
for(i in 1:nrow(subset_peaks_h)){
pktime = range(subset_peaks_h$LC_ESI_MS_Retention_Time)
pktime[1] = pktime[1] - 1.5
pktime[2] = pktime[2] + 1.5
moi = subset_peaks_h$FT_ICR_MS_mz[i]
ESI_rt_window = ESIrams$MS1[rt%between%(pktime)]
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_h_63Cu[[i]] = ms1
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_h_65Cu[[i]] = ms1
}
for(i in 1:nrow(subset_peaks_i)){
pktime = range(subset_peaks_i$LC_ESI_MS_Retention_Time)
pktime[1] = pktime[1] - 1.5
pktime[2] = pktime[2] + 1.5
moi = subset_peaks_i$FT_ICR_MS_mz[i]
ESI_rt_window = ESIrams$MS1[rt%between%(pktime)]
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_i_63Cu[[i]] = ms1
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_i_65Cu[[i]] = ms1
}
for(i in 1:nrow(subset_peaks_j)){
pktime = range(subset_peaks_j$LC_ESI_MS_Retention_Time)
pktime[1] = pktime[1] - 1.5
pktime[2] = pktime[2] + 1.5
moi = subset_peaks_j$FT_ICR_MS_mz[i]
ESI_rt_window = ESIrams$MS1[rt%between%(pktime)]
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_j_63Cu[[i]] = ms1
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_j_65Cu[[i]] = ms1
}
for(i in 1:nrow(subset_peaks_k)){
pktime = range(subset_peaks_k$LC_ESI_MS_Retention_Time)
pktime[1] = pktime[1] - 1.5
pktime[2] = pktime[2] + 1.5
moi = subset_peaks_k$FT_ICR_MS_mz[i]
ESI_rt_window = ESIrams$MS1[rt%between%(pktime)]
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_k_63Cu[[i]] = ms1
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_k_65Cu[[i]] = ms1
}
for(i in 1:nrow(subset_peaks_l)){
pktime = range(subset_peaks_l$LC_ESI_MS_Retention_Time)
pktime[1] = pktime[1] - 1.5
pktime[2] = pktime[2] + 1.5
moi = subset_peaks_l$FT_ICR_MS_mz[i]
ESI_rt_window = ESIrams$MS1[rt%between%(pktime)]
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_l_63Cu[[i]] = ms1
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_l_65Cu[[i]] = ms1
}
for(i in 1:nrow(subset_peaks_m)){
pktime = range(subset_peaks_m$LC_ESI_MS_Retention_Time)
pktime[1] = pktime[1] - 1.5
pktime[2] = pktime[2] + 1.5
moi = subset_peaks_m$FT_ICR_MS_mz[i]
ESI_rt_window = ESIrams$MS1[rt%between%(pktime)]
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_m_63Cu[[i]] = ms1
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_m_65Cu[[i]] = ms1
}
for(i in 1:nrow(subset_peaks_n)){
pktime = range(subset_peaks_n$LC_ESI_MS_Retention_Time)
pktime[1] = pktime[1] - 1.5
pktime[2] = pktime[2] + 1.5
moi = subset_peaks_n$FT_ICR_MS_mz[i]
ESI_rt_window = ESIrams$MS1[rt%between%(pktime)]
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_n_63Cu[[i]] = ms1
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_n_65Cu[[i]] = ms1
}
for(i in 1:nrow(subset_peaks_o)){
pktime = range(subset_peaks_o$LC_ESI_MS_Retention_Time)
pktime[1] = pktime[1] - 1.5
pktime[2] = pktime[2] + 1.5
moi = subset_peaks_o$FT_ICR_MS_mz[i]
ESI_rt_window = ESIrams$MS1[rt%between%(pktime)]
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_o_63Cu[[i]] = ms1
ms1 <- as.data.frame(tibble(ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$rt,
ESI_rt_window[mz%between%pmppm(moi+1.9981901, ppm=3.5)]$int))
colnames(ms1) = c("rt","int")
ms1<-as.data.frame(ksmooth(ms1[,1],ms1[,2],kernel="normal",bandwidth=.05))
colnames(ms1) = c("rt","int")
subset_peaks_o_65Cu[[i]] = ms1
}
#extract intensities
intensities_c <- sapply(subset_peaks_c_63Cu, function(df) df$int)
intensities_d <- sapply(subset_peaks_d_63Cu, function(df) df$int)
intensities_e <- sapply(subset_peaks_e_63Cu, function(df) df$int)
intensities_f <- sapply(subset_peaks_f_63Cu, function(df) df$int)
intensities_g <- sapply(subset_peaks_g_63Cu, function(df) df$int)
intensities_h <- sapply(subset_peaks_h_63Cu, function(df) df$int)
intensities_i <- sapply(subset_peaks_i_63Cu, function(df) df$int)
intensities_j <- sapply(subset_peaks_j_63Cu, function(df) df$int)
intensities_k <- sapply(subset_peaks_k_63Cu, function(df) df$int)
intensities_l <- sapply(subset_peaks_l_63Cu, function(df) df$int)
intensities_m <- sapply(subset_peaks_m_63Cu, function(df) df$int)
intensities_n <- sapply(subset_peaks_n_63Cu, function(df) df$int)
intensities_o <- sapply(subset_peaks_o_63Cu, function(df) df$int)
# Find the maximum intensity across the entire list to normalize data
max_intensity <- max(c(unlist(intensities_c),
unlist(intensities_d),
unlist(intensities_e),
unlist(intensities_f),
unlist(intensities_g),
unlist(intensities_h),
unlist(intensities_i),
unlist(intensities_j),
unlist(intensities_k),
unlist(intensities_l),
unlist(intensities_m),
unlist(intensities_n),
unlist(intensities_o)), na.rm = TRUE)
# Further formatting of the data for plotting
for (i in 1:length(subset_peaks_c_63Cu)) {
subset_peaks_c_63Cu[[i]]$type <- as.character(subset_peaks_c$FT_ICR_MS_mz[i])
subset_peaks_c_63Cu[[i]]$range = "c"
subset_peaks_c_63Cu[[i]]$metal = "63Cu"
subset_peaks_c_63Cu[[i]]$int = subset_peaks_c_63Cu[[i]]$int/max_intensity
subset_peaks_c_63Cu[[i]]$max_int = max(subset_peaks_c_63Cu[[i]]$int, na.rm = TRUE)
subset_peaks_c_63Cu[[i]]$icprt = subset_peaks_c$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_c_63Cu[[i]]$max_int) >= 0.1){
subset_peaks_c_63Cu[[i]]$size = 0.6
}else{
subset_peaks_c_63Cu[[i]]$size = 0.3
}
}
for (i in 1:length(subset_peaks_c_65Cu)) {
subset_peaks_c_65Cu[[i]]$type <- as.character(subset_peaks_c$FT_ICR_MS_mz[i])
subset_peaks_c_65Cu[[i]]$range = "c"
subset_peaks_c_65Cu[[i]]$metal = "65Cu"
subset_peaks_c_65Cu[[i]]$int = 2.2*(subset_peaks_c_65Cu[[i]]$int/max_intensity)
subset_peaks_c_65Cu[[i]]$max_int = max(subset_peaks_c_65Cu[[i]]$int, na.rm = TRUE)
subset_peaks_c_65Cu[[i]]$icprt = subset_peaks_c$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_c_65Cu[[i]]$max_int) >= 0.1){
subset_peaks_c_65Cu[[i]]$size = 0.6
}else{
subset_peaks_c_65Cu[[i]]$size = 0.3
}
}
for (i in 1:length(subset_peaks_d_63Cu)) {
subset_peaks_d_63Cu[[i]]$type <- as.character(subset_peaks_d$FT_ICR_MS_mz[i])
subset_peaks_d_63Cu[[i]]$range = "d"
subset_peaks_d_63Cu[[i]]$metal = "63Cu"
subset_peaks_d_63Cu[[i]]$int = subset_peaks_d_63Cu[[i]]$int/max_intensity
subset_peaks_d_63Cu[[i]]$max_int = max(subset_peaks_d_63Cu[[i]]$int, na.rm = TRUE)
subset_peaks_d_63Cu[[i]]$icprt = subset_peaks_d$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_d_63Cu[[i]]$max_int) >= 0.1){
subset_peaks_d_63Cu[[i]]$size = 0.6
}else{
subset_peaks_d_63Cu[[i]]$size = 0.3
}
}
for (i in 1:length(subset_peaks_d_65Cu)) {
subset_peaks_d_65Cu[[i]]$type <- as.character(subset_peaks_d$FT_ICR_MS_mz[i])
subset_peaks_d_65Cu[[i]]$range = "d"
subset_peaks_d_65Cu[[i]]$metal = "65Cu"
subset_peaks_d_65Cu[[i]]$int = 2.2*(subset_peaks_d_65Cu[[i]]$int/max_intensity)
subset_peaks_d_65Cu[[i]]$max_int = max(subset_peaks_d_65Cu[[i]]$int, na.rm = TRUE)
subset_peaks_d_65Cu[[i]]$icprt = subset_peaks_d$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_d_65Cu[[i]]$max_int) >= 0.1){
subset_peaks_d_65Cu[[i]]$size = 0.6
}else{
subset_peaks_d_65Cu[[i]]$size = 0.3
}
}
for (i in 1:length(subset_peaks_e_63Cu)) {
subset_peaks_e_63Cu[[i]]$type <- as.character(subset_peaks_e$FT_ICR_MS_mz[i])
subset_peaks_e_63Cu[[i]]$range = "e"
subset_peaks_e_63Cu[[i]]$metal = "63Cu"
subset_peaks_e_63Cu[[i]]$int = subset_peaks_e_63Cu[[i]]$int/max_intensity
subset_peaks_e_63Cu[[i]]$max_int = max(subset_peaks_e_63Cu[[i]]$int, na.rm = TRUE)
subset_peaks_e_63Cu[[i]]$icprt = subset_peaks_e$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_e_63Cu[[i]]$max_int) >= 0.1){
subset_peaks_e_63Cu[[i]]$size = 0.6
}else{
subset_peaks_e_63Cu[[i]]$size = 0.3
}
}
for (i in 1:length(subset_peaks_e_65Cu)) {
subset_peaks_e_65Cu[[i]]$type <- as.character(subset_peaks_e$FT_ICR_MS_mz[i])
subset_peaks_e_65Cu[[i]]$range = "e"
subset_peaks_e_65Cu[[i]]$metal = "65Cu"
subset_peaks_e_65Cu[[i]]$int = 2.2*(subset_peaks_e_65Cu[[i]]$int/max_intensity)
subset_peaks_e_65Cu[[i]]$max_int = max(subset_peaks_e_65Cu[[i]]$int, na.rm = TRUE)
subset_peaks_e_65Cu[[i]]$icprt = subset_peaks_e$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_e_65Cu[[i]]$max_int) >= 0.1){
subset_peaks_e_65Cu[[i]]$size = 0.6
}else{
subset_peaks_e_65Cu[[i]]$size = 0.3
}
}
for (i in 1:length(subset_peaks_f_63Cu)) {
subset_peaks_f_63Cu[[i]]$type <- as.character(subset_peaks_f$FT_ICR_MS_mz[i])
subset_peaks_f_63Cu[[i]]$range = "f"
subset_peaks_f_63Cu[[i]]$metal = "63Cu"
subset_peaks_f_63Cu[[i]]$int = subset_peaks_f_63Cu[[i]]$int/max_intensity
subset_peaks_f_63Cu[[i]]$max_int = max(subset_peaks_f_63Cu[[i]]$int, na.rm = TRUE)
subset_peaks_f_63Cu[[i]]$icprt = subset_peaks_f$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_f_63Cu[[i]]$max_int) >= 0.1){
subset_peaks_f_63Cu[[i]]$size = 0.6
}else{
subset_peaks_f_63Cu[[i]]$size = 0.3
}
}
for (i in 1:length(subset_peaks_f_65Cu)) {
subset_peaks_f_65Cu[[i]]$type <- as.character(subset_peaks_f$FT_ICR_MS_mz[i])
subset_peaks_f_65Cu[[i]]$range = "f"
subset_peaks_f_65Cu[[i]]$metal = "65Cu"
subset_peaks_f_65Cu[[i]]$int = 2.2*(subset_peaks_f_65Cu[[i]]$int/max_intensity)
subset_peaks_f_65Cu[[i]]$max_int = max(subset_peaks_f_65Cu[[i]]$int, na.rm = TRUE)
subset_peaks_f_65Cu[[i]]$icprt = subset_peaks_f$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_f_65Cu[[i]]$max_int) >= 0.1){
subset_peaks_f_65Cu[[i]]$size = 0.6
}else{
subset_peaks_f_65Cu[[i]]$size = 0.3
}
}
for (i in 1:length(subset_peaks_g_63Cu)) {
subset_peaks_g_63Cu[[i]]$type <- as.character(subset_peaks_g$FT_ICR_MS_mz[i])
subset_peaks_g_63Cu[[i]]$range = "g"
subset_peaks_g_63Cu[[i]]$metal = "63Cu"
subset_peaks_g_63Cu[[i]]$int = subset_peaks_g_63Cu[[i]]$int/max_intensity
subset_peaks_g_63Cu[[i]]$max_int = max(subset_peaks_g_63Cu[[i]]$int, na.rm = TRUE)
subset_peaks_g_63Cu[[i]]$icprt = subset_peaks_g$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_g_63Cu[[i]]$max_int) >= 0.1){
subset_peaks_g_63Cu[[i]]$size = 0.6
}else{
subset_peaks_g_63Cu[[i]]$size = 0.3
}
}
for (i in 1:length(subset_peaks_g_65Cu)) {
subset_peaks_g_65Cu[[i]]$type <- as.character(subset_peaks_g$FT_ICR_MS_mz[i])
subset_peaks_g_65Cu[[i]]$range = "g"
subset_peaks_g_65Cu[[i]]$metal = "65Cu"
subset_peaks_g_65Cu[[i]]$int = 2.2*(subset_peaks_g_65Cu[[i]]$int/max_intensity)
subset_peaks_g_65Cu[[i]]$max_int = max(subset_peaks_g_65Cu[[i]]$int, na.rm = TRUE)
subset_peaks_g_65Cu[[i]]$icprt = subset_peaks_g$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_g_65Cu[[i]]$max_int) >= 0.1){
subset_peaks_g_65Cu[[i]]$size = 0.6
}else{
subset_peaks_g_65Cu[[i]]$size = 0.3
}
}
for (i in 1:length(subset_peaks_h_63Cu)) {
subset_peaks_h_63Cu[[i]]$type <- as.character(subset_peaks_h$FT_ICR_MS_mz[i])
subset_peaks_h_63Cu[[i]]$range = "h"
subset_peaks_h_63Cu[[i]]$metal = "63Cu"
subset_peaks_h_63Cu[[i]]$int = subset_peaks_h_63Cu[[i]]$int/max_intensity
subset_peaks_h_63Cu[[i]]$max_int = max(subset_peaks_h_63Cu[[i]]$int, na.rm = TRUE)
subset_peaks_h_63Cu[[i]]$icprt = subset_peaks_h$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_h_63Cu[[i]]$max_int) >= 0.1){
subset_peaks_h_63Cu[[i]]$size = 0.6
}else{
subset_peaks_h_63Cu[[i]]$size = 0.3
}
}
for (i in 1:length(subset_peaks_h_65Cu)) {
subset_peaks_h_65Cu[[i]]$type <- as.character(subset_peaks_h$FT_ICR_MS_mz[i])
subset_peaks_h_65Cu[[i]]$range = "h"
subset_peaks_h_65Cu[[i]]$metal = "65Cu"
subset_peaks_h_65Cu[[i]]$int = 2.2*(subset_peaks_h_65Cu[[i]]$int/max_intensity)
subset_peaks_h_65Cu[[i]]$max_int = max(subset_peaks_h_65Cu[[i]]$int, na.rm = TRUE)
subset_peaks_h_65Cu[[i]]$icprt = subset_peaks_h$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_h_65Cu[[i]]$max_int) >= 0.1){
subset_peaks_h_65Cu[[i]]$size = 0.6
}else{
subset_peaks_h_65Cu[[i]]$size = 0.3
}
}
for (i in 1:length(subset_peaks_i_63Cu)) {
subset_peaks_i_63Cu[[i]]$type <- as.character(subset_peaks_i$FT_ICR_MS_mz[i])
subset_peaks_i_63Cu[[i]]$range = "i"
subset_peaks_i_63Cu[[i]]$metal = "63Cu"
subset_peaks_i_63Cu[[i]]$int = subset_peaks_i_63Cu[[i]]$int/max_intensity
subset_peaks_i_63Cu[[i]]$max_int = max(subset_peaks_i_63Cu[[i]]$int, na.rm = TRUE)
subset_peaks_i_63Cu[[i]]$icprt = subset_peaks_i$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_i_63Cu[[i]]$max_int) >= 0.1){
subset_peaks_i_63Cu[[i]]$size = 0.6
}else{
subset_peaks_i_63Cu[[i]]$size = 0.3
}
}
for (i in 1:length(subset_peaks_i_65Cu)) {
subset_peaks_i_65Cu[[i]]$type <- as.character(subset_peaks_i$FT_ICR_MS_mz[i])
subset_peaks_i_65Cu[[i]]$range = "i"
subset_peaks_i_65Cu[[i]]$metal = "65Cu"
subset_peaks_i_65Cu[[i]]$int = 2.2*(subset_peaks_i_65Cu[[i]]$int/max_intensity)
subset_peaks_i_65Cu[[i]]$max_int = max(subset_peaks_i_65Cu[[i]]$int, na.rm = TRUE)
subset_peaks_i_65Cu[[i]]$icprt = subset_peaks_i$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_i_65Cu[[i]]$max_int) >= 0.1){
subset_peaks_i_65Cu[[i]]$size = 0.6
}else{
subset_peaks_i_65Cu[[i]]$size = 0.3
}
}
for (i in 1:length(subset_peaks_j_63Cu)) {
subset_peaks_j_63Cu[[i]]$type <- as.character(subset_peaks_j$FT_ICR_MS_mz[i])
subset_peaks_j_63Cu[[i]]$range = "j"
subset_peaks_j_63Cu[[i]]$metal = "63Cu"
subset_peaks_j_63Cu[[i]]$int = subset_peaks_j_63Cu[[i]]$int/max_intensity
subset_peaks_j_63Cu[[i]]$max_int = max(subset_peaks_j_63Cu[[i]]$int, na.rm = TRUE)
subset_peaks_j_63Cu[[i]]$icprt = subset_peaks_j$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_j_63Cu[[i]]$max_int) >= 0.1){
subset_peaks_j_63Cu[[i]]$size = 0.6
}else{
subset_peaks_j_63Cu[[i]]$size = 0.3
}
}
for (i in 1:length(subset_peaks_j_65Cu)) {
subset_peaks_j_65Cu[[i]]$type <- as.character(subset_peaks_j$FT_ICR_MS_mz[i])
subset_peaks_j_65Cu[[i]]$range = "j"
subset_peaks_j_65Cu[[i]]$metal = "65Cu"
subset_peaks_j_65Cu[[i]]$int = 2.2*(subset_peaks_j_65Cu[[i]]$int/max_intensity)
subset_peaks_j_65Cu[[i]]$max_int = max(subset_peaks_j_65Cu[[i]]$int, na.rm = TRUE)
subset_peaks_j_65Cu[[i]]$icprt = subset_peaks_j$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_j_65Cu[[i]]$max_int) >= 0.1){
subset_peaks_j_65Cu[[i]]$size = 0.6
}else{
subset_peaks_j_65Cu[[i]]$size = 0.3
}
}
for (i in 1:length(subset_peaks_k_63Cu)) {
subset_peaks_k_63Cu[[i]]$type <- as.character(subset_peaks_k$FT_ICR_MS_mz[i])
subset_peaks_k_63Cu[[i]]$range = "k"
subset_peaks_k_63Cu[[i]]$metal = "63Cu"
subset_peaks_k_63Cu[[i]]$int = subset_peaks_k_63Cu[[i]]$int/max_intensity
subset_peaks_k_63Cu[[i]]$max_int = max(subset_peaks_k_63Cu[[i]]$int, na.rm = TRUE)
subset_peaks_k_63Cu[[i]]$icprt = subset_peaks_k$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_k_63Cu[[i]]$max_int) >= 0.1){
subset_peaks_k_63Cu[[i]]$size = 0.6
}else{
subset_peaks_k_63Cu[[i]]$size = 0.3
}
}
for (i in 1:length(subset_peaks_k_65Cu)) {
subset_peaks_k_65Cu[[i]]$type <- as.character(subset_peaks_k$FT_ICR_MS_mz[i])
subset_peaks_k_65Cu[[i]]$range = "k"
subset_peaks_k_65Cu[[i]]$metal = "65Cu"
subset_peaks_k_65Cu[[i]]$int = 2.2*(subset_peaks_k_65Cu[[i]]$int/max_intensity)
subset_peaks_k_65Cu[[i]]$max_int = max(subset_peaks_k_65Cu[[i]]$int, na.rm = TRUE)
subset_peaks_k_65Cu[[i]]$icprt = subset_peaks_k$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_k_65Cu[[i]]$max_int) >= 0.1){
subset_peaks_k_65Cu[[i]]$size = 0.6
}else{
subset_peaks_k_65Cu[[i]]$size = 0.3
}
}
for (i in 1:length(subset_peaks_l_63Cu)) {
subset_peaks_l_63Cu[[i]]$type <- as.character(subset_peaks_l$FT_ICR_MS_mz[i])
subset_peaks_l_63Cu[[i]]$range = "l"
subset_peaks_l_63Cu[[i]]$metal = "63Cu"
subset_peaks_l_63Cu[[i]]$int = subset_peaks_l_63Cu[[i]]$int/max_intensity
subset_peaks_l_63Cu[[i]]$max_int = max(subset_peaks_l_63Cu[[i]]$int, na.rm = TRUE)
subset_peaks_l_63Cu[[i]]$icprt = subset_peaks_l$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_l_63Cu[[i]]$max_int) >= 0.1){
subset_peaks_l_63Cu[[i]]$size = 0.6
}else{
subset_peaks_l_63Cu[[i]]$size = 0.3
}
}
for (i in 1:length(subset_peaks_l_65Cu)) {
subset_peaks_l_65Cu[[i]]$type <- as.character(subset_peaks_l$FT_ICR_MS_mz[i])
subset_peaks_l_65Cu[[i]]$range = "l"
subset_peaks_l_65Cu[[i]]$metal = "65Cu"
subset_peaks_l_65Cu[[i]]$int = 2.2*(subset_peaks_l_65Cu[[i]]$int/max_intensity)
subset_peaks_l_65Cu[[i]]$max_int = max(subset_peaks_l_65Cu[[i]]$int, na.rm = TRUE)
subset_peaks_l_65Cu[[i]]$icprt = subset_peaks_l$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_l_65Cu[[i]]$max_int) >= 0.1){
subset_peaks_l_65Cu[[i]]$size = 0.6
}else{
subset_peaks_l_65Cu[[i]]$size = 0.3
}
}
for (i in 1:length(subset_peaks_m_63Cu)) {
subset_peaks_m_63Cu[[i]]$type <- as.character(subset_peaks_m$FT_ICR_MS_mz[i])
subset_peaks_m_63Cu[[i]]$range = "m"
subset_peaks_m_63Cu[[i]]$metal = "63Cu"
subset_peaks_m_63Cu[[i]]$int = subset_peaks_m_63Cu[[i]]$int/max_intensity
subset_peaks_m_63Cu[[i]]$max_int = max(subset_peaks_m_63Cu[[i]]$int, na.rm = TRUE)
subset_peaks_m_63Cu[[i]]$icprt = subset_peaks_m$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_m_63Cu[[i]]$max_int) >= 0.1){
subset_peaks_m_63Cu[[i]]$size = 0.6
}else{
subset_peaks_m_63Cu[[i]]$size = 0.3
}
}
for (i in 1:length(subset_peaks_m_65Cu)) {
subset_peaks_m_65Cu[[i]]$type <- as.character(subset_peaks_m$FT_ICR_MS_mz[i])
subset_peaks_m_65Cu[[i]]$range = "m"
subset_peaks_m_65Cu[[i]]$metal = "65Cu"
subset_peaks_m_65Cu[[i]]$int = 2.2*(subset_peaks_m_65Cu[[i]]$int/max_intensity)
subset_peaks_m_65Cu[[i]]$max_int = max(subset_peaks_m_65Cu[[i]]$int, na.rm = TRUE)
subset_peaks_m_65Cu[[i]]$icprt = subset_peaks_m$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_m_65Cu[[i]]$max_int) >= 0.1){
subset_peaks_m_65Cu[[i]]$size = 0.6
}else{
subset_peaks_m_65Cu[[i]]$size = 0.3
}
}
for (i in 1:length(subset_peaks_n_63Cu)) {
subset_peaks_n_63Cu[[i]]$type <- as.character(subset_peaks_n$FT_ICR_MS_mz[i])
subset_peaks_n_63Cu[[i]]$range = "n"
subset_peaks_n_63Cu[[i]]$metal = "63Cu"
subset_peaks_n_63Cu[[i]]$int = subset_peaks_n_63Cu[[i]]$int/max_intensity
subset_peaks_n_63Cu[[i]]$max_int = max(subset_peaks_n_63Cu[[i]]$int, na.rm = TRUE)
subset_peaks_n_63Cu[[i]]$icprt = subset_peaks_n$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_n_63Cu[[i]]$max_int) >= 0.1){
subset_peaks_n_63Cu[[i]]$size = 0.6
}else{
subset_peaks_n_63Cu[[i]]$size = 0.3
}
}
for (i in 1:length(subset_peaks_n_65Cu)) {
subset_peaks_n_65Cu[[i]]$type <- as.character(subset_peaks_n$FT_ICR_MS_mz[i])
subset_peaks_n_65Cu[[i]]$range = "n"
subset_peaks_n_65Cu[[i]]$metal = "65Cu"
subset_peaks_n_65Cu[[i]]$int = 2.2*(subset_peaks_n_65Cu[[i]]$int/max_intensity)
subset_peaks_n_65Cu[[i]]$max_int = max(subset_peaks_n_65Cu[[i]]$int, na.rm = TRUE)
subset_peaks_n_65Cu[[i]]$icprt = subset_peaks_n$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_n_65Cu[[i]]$max_int) >= 0.1){
subset_peaks_n_65Cu[[i]]$size = 0.6
}else{
subset_peaks_n_65Cu[[i]]$size = 0.3
}
}
for (i in 1:length(subset_peaks_o_63Cu)) {
subset_peaks_o_63Cu[[i]]$type <- as.character(subset_peaks_o$FT_ICR_MS_mz[i])
subset_peaks_o_63Cu[[i]]$range = "o"
subset_peaks_o_63Cu[[i]]$metal = "63Cu"
subset_peaks_o_63Cu[[i]]$int = subset_peaks_o_63Cu[[i]]$int/max_intensity
subset_peaks_o_63Cu[[i]]$max_int = max(subset_peaks_o_63Cu[[i]]$int, na.rm = TRUE)
subset_peaks_o_63Cu[[i]]$icprt = subset_peaks_o$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_o_63Cu[[i]]$max_int) >= 0.1){
subset_peaks_o_63Cu[[i]]$size = 0.6
}else{
subset_peaks_o_63Cu[[i]]$size = 0.3
}
}
for (i in 1:length(subset_peaks_o_65Cu)) {
subset_peaks_o_65Cu[[i]]$type <- as.character(subset_peaks_o$FT_ICR_MS_mz[i])
subset_peaks_o_65Cu[[i]]$range = "o"
subset_peaks_o_65Cu[[i]]$metal = "65Cu"
subset_peaks_o_65Cu[[i]]$int = 2.2*(subset_peaks_o_65Cu[[i]]$int/max_intensity)
subset_peaks_o_65Cu[[i]]$max_int = max(subset_peaks_o_65Cu[[i]]$int, na.rm = TRUE)
subset_peaks_o_65Cu[[i]]$icprt = subset_peaks_o$LC_ESI_MS_Retention_Time[i]
if(unique(subset_peaks_o_65Cu[[i]]$max_int) >= 0.1){
subset_peaks_o_65Cu[[i]]$size = 0.6
}else{
subset_peaks_o_65Cu[[i]]$size = 0.3
}
}
# Combine all data frames
combined_df <- do.call(rbind, c(subset_peaks_c_63Cu,
subset_peaks_c_65Cu,
subset_peaks_d_63Cu,
subset_peaks_d_65Cu,
subset_peaks_e_63Cu,
subset_peaks_e_65Cu,
subset_peaks_f_63Cu,
subset_peaks_f_65Cu,
subset_peaks_g_63Cu,
subset_peaks_g_65Cu,
subset_peaks_h_63Cu,
subset_peaks_h_65Cu,
subset_peaks_i_63Cu,
subset_peaks_i_65Cu,
subset_peaks_j_63Cu,
subset_peaks_j_65Cu,
subset_peaks_k_63Cu,
subset_peaks_k_65Cu,
subset_peaks_l_63Cu,
subset_peaks_l_65Cu,
subset_peaks_m_63Cu,
subset_peaks_m_65Cu,
subset_peaks_n_63Cu,
subset_peaks_n_65Cu,
subset_peaks_o_63Cu,
subset_peaks_o_65Cu))
masses_to_plot = data.frame(matrix(NA, # Create empty data frame
nrow = length(unique(combined_df$type)),
ncol = ncol(combined_df)))
colnames(masses_to_plot) = colnames(combined_df)
for(i in 1:nrow(masses_to_plot)){
subset_combined_df = combined_df %>%
filter(type == unique(combined_df$type)[i] & metal == "63Cu") %>%
filter(int == unique(max_int))
masses_to_plot[i,] = subset_combined_df
}
c10 = c("#1F78B4","#33A02C","#E31A1C","#FF7F00","#6A3D9A","#B15928","#E1AF00","#6A8155","#F7B1A8","black")
combined_df$type = factor(combined_df$type, levels = sort(as.numeric(levels(factor(combined_df$type)))))
#making actual EIC plots
c = ggplot() +
geom_line(data = combined_df %>% filter(range == "c"),
aes(x = rt,
y = int,
color = type,
alpha = metal),
size = 1.5) +
scale_color_manual(values = c10[1:length(unique((combined_df %>% filter(range == "c"))$type))], guide = "none") +
scale_alpha_manual(values = c(1,0.25), guide = "none") +
scale_x_continuous(limits = c(unique((plot_peaks %>% filter(Peak == "c"))$LC_ICP_MS_Retention_Time)-1.5, unique((plot_peaks %>% filter(Peak == "c"))$LC_ICP_MS_Retention_Time)+1.5),
) +
scale_y_continuous(labels = scales::number_format(accuracy = 0.001, decimal.mark = '.'),
limits = c(min((combined_df %>% filter(range == "c"))$int, na.rm=TRUE),max((combined_df %>% filter(range == "c"))$max_int)*1.5)) +
labs(x = "Retention Time (min)",
y = "LC-ESI-MS (Orbitrap)\nRelative Intensity") +
geom_vline(data = plot_peaks %>% filter(Peak == "c"),
aes(xintercept = unique(LC_ICP_MS_Retention_Time)),
linetype = "dashed", size = 0.8,
color = "gray65") +
geom_text(data = plot_peaks %>% filter(Peak == "c"),
aes(x = unique(LC_ICP_MS_Retention_Time),
label = "c",
y = max((combined_df %>% filter(range == "c"))$int, na.rm = TRUE)*1.25),
fontface = "bold",
color = "black",
size = 8) +
geom_point(data = masses_to_plot %>% filter(range == "c") %>% arrange(icprt),
aes(x = icprt,
y = -1,
color = type)) +
guides(color = guide_legend(override.aes = list(shape = 16, linetype = "blank", alpha = 1, size = 2.5), ncol = 1, title = "")) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 20) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.text = element_text(face = "bold"),
legend.position = c(1,1.075),
legend.justification = c(1,1),
legend.background = element_blank())
d = ggplot() +
geom_line(data = combined_df %>% filter(range == "d"),
aes(x = rt,
y = int,
color = type,
alpha = metal),
size = 1.5) +
scale_color_manual(values = c10[1:length(unique((combined_df %>% filter(range == "d"))$type))], guide = "none") +
scale_alpha_manual(values = c(1,0.25), guide = "none") +
scale_x_continuous(limits = c(unique((plot_peaks %>% filter(Peak == "d"))$LC_ICP_MS_Retention_Time)-1.5, unique((plot_peaks %>% filter(Peak == "d"))$LC_ICP_MS_Retention_Time)+1.5),
) +
scale_y_continuous(labels = scales::number_format(accuracy = 0.001, decimal.mark = '.'),
limits = c(min((combined_df %>% filter(range == "d"))$int, na.rm=TRUE),max((combined_df %>% filter(range == "d"))$max_int)*1.5)) +
labs(x = "Retention Time (min)",
y = "LC-ESI-MS (Orbitrap)\nRelative Intensity") +
geom_vline(data = plot_peaks %>% filter(Peak == "d"),
aes(xintercept = unique(LC_ICP_MS_Retention_Time)),
linetype = "dashed", size = 0.8,
color = "gray65") +
geom_text(data = plot_peaks %>% filter(Peak == "d"),
aes(x = unique(LC_ICP_MS_Retention_Time),
label = "d",
y = max((combined_df %>% filter(range == "d"))$int, na.rm = TRUE)*1.25),
fontface = "bold",
color = "black",
size = 8) +
geom_point(data = masses_to_plot %>% filter(range == "d") %>% arrange(icprt),
aes(x = icprt,
y = -1,
color = type)) +
guides(color = guide_legend(override.aes = list(shape = 16, linetype = "blank", alpha = 1, size = 2.5), ncol = 1, title = "")) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 20) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.text = element_text(face = "bold"),
legend.position = c(1,1.075),
legend.justification = c(1,1),
legend.background = element_blank())
e = ggplot() +
geom_line(data = combined_df %>% filter(range == "e"),
aes(x = rt,
y = int,
color = type,
alpha = metal),
size = 1.5) +
scale_color_manual(values = c10[1:length(unique((combined_df %>% filter(range == "e"))$type))], guide = "none") +
scale_alpha_manual(values = c(1,0.25), guide = "none") +
scale_x_continuous(limits = c(unique((plot_peaks %>% filter(Peak == "e"))$LC_ICP_MS_Retention_Time)-1.5, unique((plot_peaks %>% filter(Peak == "e"))$LC_ICP_MS_Retention_Time)+1.5),
breaks = c(19,19.5,20,20.5,21,21.5)) +
scale_y_continuous(labels = scales::number_format(accuracy = 0.001, decimal.mark = '.'),
limits = c(min((combined_df %>% filter(range == "e"))$int, na.rm=TRUE),max((combined_df %>% filter(range == "e"))$max_int)*1.5)) +
labs(x = "Retention Time (min)",
y = "LC-ESI-MS (Orbitrap)\nRelative Intensity") +
geom_vline(data = plot_peaks %>% filter(Peak == "e"),
aes(xintercept = unique(LC_ICP_MS_Retention_Time)),
linetype = "dashed", size = 0.8,
color = "gray65") +
geom_text(data = plot_peaks %>% filter(Peak == "e"),
aes(x = unique(LC_ICP_MS_Retention_Time),
label = "e",
y = max((combined_df %>% filter(range == "e"))$int, na.rm = TRUE)*1.25),
fontface = "bold",
color = "black",
size = 8) +
geom_point(data = masses_to_plot %>% filter(range == "e") %>% arrange(icprt),
aes(x = icprt,
y = -1,
color = type)) +
guides(color = guide_legend(override.aes = list(shape = 16, linetype = "blank", alpha = 1, size = 2.5), ncol = 1, title = "")) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 20) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.text = element_text(face = "bold"),
legend.position = c(1,1.075),
legend.justification = c(1,1),
legend.background = element_blank())
f = ggplot() +
geom_line(data = combined_df %>% filter((range == "f" & metal == "65Cu" & rt <= 21.8) | (range == "f" & metal == "63Cu")),
aes(x = rt,
y = int,
color = type,
alpha = metal),
size = 1.5) +
scale_color_manual(values = c10[1:length(unique((combined_df %>% filter(range == "f"))$type))], guide = "none") +
scale_alpha_manual(values = c(1,0.25), guide = "none") +
scale_x_continuous(limits = c(unique((plot_peaks %>% filter(Peak == "f"))$LC_ICP_MS_Retention_Time)-1.5, unique((plot_peaks %>% filter(Peak == "f"))$LC_ICP_MS_Retention_Time)+1.5),
) +
scale_y_continuous(labels = scales::number_format(accuracy = 0.001, decimal.mark = '.'),
limits = c(min((combined_df %>% filter(range == "f"))$int, na.rm=TRUE),max((combined_df %>% filter(range == "f"))$max_int)*1.5),
breaks = c(0.025,0.05,0.075,0.1,1.025)) +
labs(x = "Retention Time (min)",
y = "LC-ESI-MS (Orbitrap)\nRelative Intensity") +
geom_vline(data = plot_peaks %>% filter(Peak == "f"),
aes(xintercept = unique(LC_ICP_MS_Retention_Time)),
linetype = "dashed", size = 0.8,
color = "gray65") +
geom_text(data = plot_peaks %>% filter(Peak == "f"),
aes(x = unique(LC_ICP_MS_Retention_Time),
label = "f",
y = max((combined_df %>% filter(range == "f"))$int, na.rm = TRUE)*1.25),
fontface = "bold",
color = "black",
size = 8) +
geom_point(data = masses_to_plot %>% filter(range == "f") %>% arrange(icprt),
aes(x = icprt,
y = -1,
color = type)) +
guides(color = guide_legend(override.aes = list(shape = 16, linetype = "blank", alpha = 1, size = 2.5), ncol = 1, title = "")) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 20) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.text = element_text(face = "bold"),
legend.position = c(1,1.075),
legend.justification = c(1,1),
legend.background = element_blank())
g = ggplot() +
geom_line(data = combined_df %>% filter(range == "g"),
aes(x = rt,
y = int,
color = type,
alpha = metal),
size = 1.5) +
scale_color_manual(values = c10[1:length(unique((combined_df %>% filter(range == "g"))$type))], guide = "none") +
scale_alpha_manual(values = c(1,0.25), guide = "none") +
scale_x_continuous(limits = c(unique((plot_peaks %>% filter(Peak == "g"))$LC_ICP_MS_Retention_Time)-1.5, unique((plot_peaks %>% filter(Peak == "g"))$LC_ICP_MS_Retention_Time)+1.5),
) +
scale_y_continuous(labels = scales::number_format(accuracy = 0.001, decimal.mark = '.'),
limits = c(min((combined_df %>% filter(range == "g"))$int, na.rm=TRUE),max((combined_df %>% filter(range == "g"))$max_int)*1.5)) +
labs(x = "Retention Time (min)",
y = "LC-ESI-MS (Orbitrap)\nRelative Intensity") +
geom_vline(data = plot_peaks %>% filter(Peak == "g"),
aes(xintercept = unique(LC_ICP_MS_Retention_Time)),
linetype = "dashed", size = 0.8,
color = "gray65") +
geom_text(data = plot_peaks %>% filter(Peak == "g"),
aes(x = unique(LC_ICP_MS_Retention_Time),
label = "g",
y = max((combined_df %>% filter(range == "g"))$int, na.rm = TRUE)*1.25),
fontface = "bold",
color = "black",
size = 8) +
geom_point(data = masses_to_plot %>% filter(range == "g") %>% arrange(icprt),
aes(x = icprt,
y = -1,
color = type)) +
guides(color = guide_legend(override.aes = list(shape = 16, linetype = "blank", alpha = 1, size = 2.5), ncol = 1, title = "")) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 20) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.text = element_text(face = "bold"),
legend.position = c(1,1.075),
legend.justification = c(1,1),
legend.background = element_blank())
h = ggplot() +
geom_line(data = combined_df %>% filter(range == "h"),
aes(x = rt,
y = int,
color = type,
alpha = metal),
size = 1.5) +
scale_color_manual(values = c10[1:length(unique((combined_df %>% filter(range == "h"))$type))], guide = "none") +
scale_alpha_manual(values = c(1,0.25), guide = "none") +
scale_x_continuous(limits = c(unique((plot_peaks %>% filter(Peak == "h"))$LC_ICP_MS_Retention_Time)-1.5, unique((plot_peaks %>% filter(Peak == "h"))$LC_ICP_MS_Retention_Time)+1.5),
breaks = c(22.5,23,23.5,24,24.5,25)) +
scale_y_continuous(labels = scales::number_format(accuracy = 0.001, decimal.mark = '.'),
limits = c(min((combined_df %>% filter(range == "h"))$int, na.rm=TRUE),max((combined_df %>% filter(range == "h"))$max_int)*1.5)) +
labs(x = "Retention Time (min)",
y = "LC-ESI-MS (Orbitrap)\nRelative Intensity") +
geom_vline(data = plot_peaks %>% filter(Peak == "h"),
aes(xintercept = unique(LC_ICP_MS_Retention_Time)),
linetype = "dashed", size = 0.8,
color = "gray65") +
geom_text(data = plot_peaks %>% filter(Peak == "h"),
aes(x = unique(LC_ICP_MS_Retention_Time),
label = "h",
y = max((combined_df %>% filter(range == "h"))$int, na.rm = TRUE)*1.25),
fontface = "bold",
color = "black",
size = 8) +
geom_point(data = masses_to_plot %>% filter(range == "h") %>% arrange(icprt),
aes(x = icprt,
y = -1,
color = type)) +
guides(color = guide_legend(override.aes = list(shape = 16, linetype = "blank", alpha = 1, size = 2.5), ncol = 1, title = "")) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 20) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.text = element_text(face = "bold"),
legend.position = c(1,1.075),
legend.justification = c(1,1),
legend.background = element_blank())
i = ggplot() +
geom_line(data = combined_df %>% filter(range == "i"),
aes(x = rt,
y = int,
color = type,
alpha = metal),
size = 1.5) +
scale_color_manual(values = c10[1:length(unique((combined_df %>% filter(range == "i"))$type))], guide = "none") +
scale_alpha_manual(values = c(1,0.25), guide = "none") +
scale_x_continuous(limits = c(unique((plot_peaks %>% filter(Peak == "i"))$LC_ICP_MS_Retention_Time)-1.5, unique((plot_peaks %>% filter(Peak == "i"))$LC_ICP_MS_Retention_Time)+1.5),
breaks = c(23.5,24,24.5,25,25.5,26)) +
scale_y_continuous(labels = scales::number_format(accuracy = 0.001, decimal.mark = '.'),
limits = c(min((combined_df %>% filter(range == "i"))$int, na.rm=TRUE),max((combined_df %>% filter(range == "i"))$max_int)*1.5)) +
labs(x = "Retention Time (min)",
y = "LC-ESI-MS (Orbitrap)\nRelative Intensity") +
geom_vline(data = plot_peaks %>% filter(Peak == "i"),
aes(xintercept = unique(LC_ICP_MS_Retention_Time)),
linetype = "dashed", size = 0.8,
color = "gray65") +
geom_text(data = plot_peaks %>% filter(Peak == "i"),
aes(x = unique(LC_ICP_MS_Retention_Time),
label = "i",
y = max((combined_df %>% filter(range == "i"))$int, na.rm = TRUE)*1.25),
fontface = "bold",
color = "black",
size = 8) +
geom_point(data = masses_to_plot %>% filter(range == "i") %>% arrange(icprt),
aes(x = icprt,
y = -1,
color = type)) +
guides(color = guide_legend(override.aes = list(shape = 16, linetype = "blank", alpha = 1, size = 2.5), ncol = 1, title = "")) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 20) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.text = element_text(face = "bold"),
legend.position = c(1,1.075),
legend.justification = c(1,1),
legend.background = element_blank())
j = ggplot() +
geom_line(data = combined_df %>% filter(range == "j"),
aes(x = rt,
y = int,
color = type,
alpha = metal),
size = 1.5) +
scale_color_manual(values = c10[1:length(unique((combined_df %>% filter(range == "j"))$type))], guide = "none") +
scale_alpha_manual(values = c(1,0.25), guide = "none") +
scale_x_continuous(limits = c(unique((plot_peaks %>% filter(Peak == "j"))$LC_ICP_MS_Retention_Time)-1.5, unique((plot_peaks %>% filter(Peak == "j"))$LC_ICP_MS_Retention_Time)+1.5),
breaks = c(24.5,25,25.5,26,26.5,27)) +
scale_y_continuous(labels = scales::number_format(accuracy = 0.001, decimal.mark = '.'),
limits = c(min((combined_df %>% filter(range == "j"))$int, na.rm=TRUE),max((combined_df %>% filter(range == "j"))$max_int)*1.5)) +
labs(x = "Retention Time (min)",
y = "LC-ESI-MS (Orbitrap)\nRelative Intensity") +
geom_vline(data = plot_peaks %>% filter(Peak == "j"),
aes(xintercept = unique(LC_ICP_MS_Retention_Time)),
linetype = "dashed", size = 0.8,
color = "gray65") +
geom_text(data = plot_peaks %>% filter(Peak == "j"),
aes(x = unique(LC_ICP_MS_Retention_Time),
label = "j",
y = max((combined_df %>% filter(range == "j"))$int, na.rm = TRUE)*1.25),
fontface = "bold",
color = "black",
size = 8) +
geom_point(data = masses_to_plot %>% filter(range == "j") %>% arrange(icprt),
aes(x = icprt,
y = -1,
color = type)) +
guides(color = guide_legend(override.aes = list(shape = 16, linetype = "blank", alpha = 1, size = 2.5), ncol = 1, title = "")) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 20) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.text = element_text(face = "bold"),
legend.position = c(1.1,1.075),
legend.justification = c(1,1),
legend.background = element_rect(fill = "transparent"))
k = ggplot() +
geom_line(data = combined_df %>% filter(range == "k"),
aes(x = rt,
y = int,
color = type,
alpha = metal),
size = 1.5) +
scale_color_manual(values = c10[1:length(unique((combined_df %>% filter(range == "k"))$type))], guide = "none") +
scale_alpha_manual(values = c(1,0.25), guide = "none") +
scale_x_continuous(limits = c(unique((plot_peaks %>% filter(Peak == "k"))$LC_ICP_MS_Retention_Time)-1.5, unique((plot_peaks %>% filter(Peak == "k"))$LC_ICP_MS_Retention_Time)+1.5),
breaks = c(25,25.5,26,26.5,27,27.5)) +
scale_y_continuous(labels = scales::number_format(accuracy = 0.001, decimal.mark = '.'),
limits = c(min((combined_df %>% filter(range == "k"))$int, na.rm=TRUE),max((combined_df %>% filter(range == "k"))$max_int)*1.5)) +
labs(x = "Retention Time (min)",
y = "LC-ESI-MS (Orbitrap)\nRelative Intensity") +
geom_vline(data = plot_peaks %>% filter(Peak == "k"),
aes(xintercept = unique(LC_ICP_MS_Retention_Time)),
linetype = "dashed", size = 0.8,
color = "gray65") +
geom_text(data = plot_peaks %>% filter(Peak == "k"),
aes(x = unique(LC_ICP_MS_Retention_Time),
label = "k",
y = max((combined_df %>% filter(range == "k"))$int, na.rm = TRUE)*1.25),
fontface = "bold",
color = "black",
size = 8) +
geom_point(data = masses_to_plot %>% filter(range == "k") %>% arrange(icprt),
aes(x = icprt,
y = -1,
color = type)) +
guides(color = guide_legend(override.aes = list(shape = 16, linetype = "blank", alpha = 1, size = 2.5), ncol = 1, title = "")) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 20) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.text = element_text(face = "bold"),
legend.position = c(1,1.075),
legend.justification = c(1,1),
legend.background = element_blank())
l = ggplot() +
geom_line(data = combined_df %>% filter(range == "l"),
aes(x = rt,
y = int,
color = type,
alpha = metal),
size = 1.5) +
scale_color_manual(values = c10[1:length(unique((combined_df %>% filter(range == "l"))$type))], guide = "none") +
scale_alpha_manual(values = c(1,0.25), guide = "none") +
scale_x_continuous(limits = c(unique((plot_peaks %>% filter(Peak == "l"))$LC_ICP_MS_Retention_Time)-1.5, unique((plot_peaks %>% filter(Peak == "l"))$LC_ICP_MS_Retention_Time)+1.5),
breaks = c(26,26.5,27,27.5,28)) +
scale_y_continuous(labels = scales::number_format(accuracy = 0.001, decimal.mark = '.'),
limits = c(min((combined_df %>% filter(range == "l"))$int, na.rm=TRUE),max((combined_df %>% filter(range == "l"))$max_int)*1.5)) +
labs(x = "Retention Time (min)",
y = "LC-ESI-MS (Orbitrap)\nRelative Intensity") +
geom_vline(data = plot_peaks %>% filter(Peak == "l"),
aes(xintercept = unique(LC_ICP_MS_Retention_Time)),
linetype = "dashed", size = 0.8,
color = "gray65") +
geom_text(data = plot_peaks %>% filter(Peak == "l"),
aes(x = unique(LC_ICP_MS_Retention_Time),
label = "l",
y = max((combined_df %>% filter(range == "l"))$int, na.rm = TRUE)*1.25),
fontface = "bold",
color = "black",
size = 8) +
geom_point(data = masses_to_plot %>% filter(range == "l") %>% arrange(icprt),
aes(x = icprt,
y = -1,
color = type)) +
guides(color = guide_legend(override.aes = list(shape = 16, linetype = "blank", alpha = 1, size = 2.5), ncol = 1, title = "")) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 20) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.text = element_text(face = "bold"),
legend.position = c(1,1.075),
legend.justification = c(1,1),
legend.background = element_blank())
m = ggplot() +
geom_line(data = combined_df %>% filter(range == "m"),
aes(x = rt,
y = int,
color = type,
alpha = metal),
size = 1.5) +
scale_color_manual(values = c10[1:length(unique((combined_df %>% filter(range == "m"))$type))], guide = "none") +
scale_alpha_manual(values = c(1,0.25), guide = "none") +
scale_x_continuous(limits = c(unique((plot_peaks %>% filter(Peak == "m"))$LC_ICP_MS_Retention_Time)-1.5, unique((plot_peaks %>% filter(Peak == "m"))$LC_ICP_MS_Retention_Time)+1.5),
breaks = c(27,27.5,28,28.5,29,29.5)) +
scale_y_continuous(labels = scales::number_format(accuracy = 0.001, decimal.mark = '.'),
limits = c(min((combined_df %>% filter(range == "m"))$int, na.rm=TRUE),max((combined_df %>% filter(range == "m"))$max_int)*1.5),
breaks = c(0.25,0.5,0.75,1)) +
labs(x = "Retention Time (min)",
y = "LC-ESI-MS (Orbitrap)\nRelative Intensity") +
geom_vline(data = plot_peaks %>% filter(Peak == "m"),
aes(xintercept = unique(LC_ICP_MS_Retention_Time)),
linetype = "dashed", size = 0.8,
color = "gray65") +
geom_text(data = plot_peaks %>% filter(Peak == "m"),
aes(x = unique(LC_ICP_MS_Retention_Time),
label = "m",
y = max((combined_df %>% filter(range == "m"))$int, na.rm = TRUE)*1.25),
fontface = "bold",
color = "black",
size = 8) +
geom_point(data = masses_to_plot %>% filter(range == "m") %>% arrange(icprt),
aes(x = icprt,
y = -1,
color = type)) +
guides(color = guide_legend(override.aes = list(shape = 16, linetype = "blank", alpha = 1, size = 2.5), ncol = 1, title = "")) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 20) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.text = element_text(face = "bold"),
legend.position = c(-0.025,1.075),
legend.justification = c(0,1),
legend.background = element_blank())
n = ggplot() +
geom_line(data = combined_df %>% filter(range == "n"),
aes(x = rt,
y = int,
color = type,
alpha = metal),
size = 1.5) +
scale_color_manual(values = c10[1:length(unique((combined_df %>% filter(range == "n"))$type))], guide = "none") +
scale_alpha_manual(values = c(1,0.25), guide = "none") +
scale_x_continuous(limits = c(unique((plot_peaks %>% filter(Peak == "n"))$LC_ICP_MS_Retention_Time)-1.5, unique((plot_peaks %>% filter(Peak == "n"))$LC_ICP_MS_Retention_Time)+1.5),
) +
scale_y_continuous(labels = scales::number_format(accuracy = 0.001, decimal.mark = '.'),
limits = c(min((combined_df %>% filter(range == "n"))$int, na.rm=TRUE),max((combined_df %>% filter(range == "n"))$max_int)*1.5),
breaks = c(0.25,0.5,0.75,1)) +
labs(x = "Retention Time (min)",
y = "LC-ESI-MS (Orbitrap)\nRelative Intensity") +
geom_vline(data = plot_peaks %>% filter(Peak == "n"),
aes(xintercept = unique(LC_ICP_MS_Retention_Time)),
linetype = "dashed", size = 0.8,
color = "gray65") +
geom_text(data = plot_peaks %>% filter(Peak == "n"),
aes(x = unique(LC_ICP_MS_Retention_Time),
label = "n",
y = max((combined_df %>% filter(range == "n"))$int, na.rm = TRUE)*1.25),
fontface = "bold",
color = "black",
size = 8) +
geom_point(data = masses_to_plot %>% filter(range == "n") %>% arrange(icprt),
aes(x = icprt,
y = -1,
color = type)) +
guides(color = guide_legend(override.aes = list(shape = 16, linetype = "blank", alpha = 1, size = 2.5), ncol = 1, title = "")) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 20) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.text = element_text(face = "bold"),
legend.position = c(1,1.075),
legend.justification = c(1,1),
legend.background = element_blank())
o = ggplot() +
geom_line(data = combined_df %>% filter(range == "o"),
aes(x = rt,
y = int,
color = type,
alpha = metal),
size = 1.5) +
scale_color_manual(values = c10[1:length(unique((combined_df %>% filter(range == "o"))$type))], guide = "none") +
scale_alpha_manual(values = c(1,0.25), guide = "none") +
scale_x_continuous(limits = c(unique((plot_peaks %>% filter(Peak == "o"))$LC_ICP_MS_Retention_Time)-1.5, unique((plot_peaks %>% filter(Peak == "o"))$LC_ICP_MS_Retention_Time)+1.5),
breaks = c(29.5,30,30.5,31,31.5,32)) +
scale_y_continuous(labels = scales::number_format(accuracy = 0.001, decimal.mark = '.'),
limits = c(min((combined_df %>% filter(range == "o"))$int, na.rm=TRUE),max((combined_df %>% filter(range == "o"))$max_int)*1.5)) +
labs(x = "Retention Time (min)",
y = "LC-ESI-MS (Orbitrap)\nRelative Intensity") +
geom_vline(data = plot_peaks %>% filter(Peak == "o"),
aes(xintercept = unique(LC_ICP_MS_Retention_Time)),
linetype = "dashed", size = 0.8,
color = "gray65") +
geom_text(data = plot_peaks %>% filter(Peak == "o"),
aes(x = unique(LC_ICP_MS_Retention_Time),
label = "o",
y = max((combined_df %>% filter(range == "o"))$int, na.rm = TRUE)*1.25),
fontface = "bold",
color = "black",
size = 8) +
geom_point(data = masses_to_plot %>% filter(range == "o") %>% arrange(icprt),
aes(x = icprt,
y = -1,
color = type)) +
guides(color = guide_legend(override.aes = list(shape = 16, linetype = "blank", alpha = 1, size = 2.5), ncol = 1, title = "")) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 20) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.text = element_text(face = "bold"),
legend.position = c(1,1.075),
legend.justification = c(1,1))
#LCICPMS plot for Fig. 2A
a = ggplot() +
geom_line(data = as.data.frame(ICP_smoothish),
aes(x = x/60, y = y), color = "black", size = 1.5) +
scale_x_continuous(limits = c(13.5,32), breaks = c(14,16,18,20,22,24,26,28,30,32)) +
scale_y_continuous(labels = scales::scientific, limits = c(0, 1.07E5), breaks = c(0,3E4,6E4,9E4)) +
labs(x = "Retention Time (min)",
y = "<b><span style = 'color:#000000;'>LC-ICP-MS</span><span style = 'color:#000000;'><sup>63</sup>Cu</span><br><span style = 'color:#000000;'>Intensity (cps)</span></b>") +
geom_vline(data = plot_peaks,
aes(xintercept = LC_ICP_MS_Retention_Time), color = "gray65", linetype = "dashed", size = 0.8) +
geom_text(data = plot_peaks,
aes(x = LC_ICP_MS_Retention_Time, y = 1.5E4, label = Peak), fontface = "bold", size = 8, color = "black") +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 20) +
theme(axis.title.x = element_text(face = "bold"),
#axis.title.y.left = element_text(face = "bold"),
axis.title.y.left = element_markdown(),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"))
Fig2 = plot_grid(a,
plot_grid(d + theme(axis.title.x = element_blank()), e + theme(axis.title.x = element_blank(), axis.title.y.left = element_blank()), f + theme(axis.title.x = element_blank(), axis.title.y.left = element_blank()),
g + theme(axis.title.x = element_blank()), h + theme(axis.title.x = element_blank(), axis.title.y.left = element_blank()), i + theme(axis.title.x = element_blank(), axis.title.y.left = element_blank()),
j + theme(axis.title.x = element_blank()), k + theme(axis.title.x = element_blank(), axis.title.y.left = element_blank()), l + theme(axis.title.x = element_blank(), axis.title.y.left = element_blank()),
m, n + theme(axis.title.y.left = element_blank()), o + theme(axis.title.y.left = element_blank()),
ncol = 3,
align = "hv"),
align = "h",
nrow = 2,
rel_heights = c(1/6,5/6),
labels = c("(A)","(B)"),
label_size = 24,
hjust = -.75, vjust = 1
)
Fig2
Figure 3
Van Krevelen-like diagrams of fecal Cu ligands and known tetrapyrroles. Recent multidimensional stoichiometric compound classification (MSCC) constraints are denoted by shaded regions, although it should be noted these constraints also include phosphorous ratios, mass, and number of atoms in the complete classification scheme (Rivas-Ubach et al., 2018)
#O/C vs H/C
vk_a = ggplot() +
geom_rect(aes(xmin = 0, xmax = 0.8, ymin = 0.75, ymax = 2.05), fill = "white") +
geom_rect(aes(xmin = 0, xmax = 0.8, ymin = 0.75, ymax = 1.31), fill = "#C9211D", alpha = 0.3) +
geom_rect(aes(xmin = 0.5, xmax = 0.8, ymin = 1, ymax = 1.8), fill = "#288674", alpha = 0.3) +
geom_rect(aes(xmin = 0.61, xmax = 0.8, ymin = 1.45, ymax = 2.05), fill = "#F2A800", alpha = 0.3) +
geom_rect(aes(xmin = 0, xmax = 0.605, ymin = 1.31, ymax = 2.05), fill = "#F88600", alpha = 0.3) +
geom_rect(aes(xmin = 0.12, xmax = 0.605, ymin = 0.9, ymax = 2.05), fill = "#5BBCD6", alpha = 0.3) +
geom_rect(aes(xmin = 0.605, xmax = 0.8, ymin = 1.2, ymax = 2.05), fill = "#5BBCD6", alpha = 0.3) +
geom_segment(aes(x = 0, xend = 0.8, y = 1.305, yend = 1.305), linetype = "dashed", color = "#C9211D") +
geom_segment(aes(x = 0.5, xend = 0.5, y = 1, yend = 1.8), linetype = "dashed", color = "#288674") +
geom_segment(aes(x = 0.5, xend = 0.8, y = 1, yend = 1), linetype = "dashed", color = "#288674") +
geom_segment(aes(x = 0.5, xend = 0.8, y = 1.8, yend = 1.8), linetype = "dashed", color = "#288674") +
geom_segment(aes(x = 0.61, xend = 0.61, y = 1.45, yend = 2.05), linetype = "dashed", color = "#F2A800") +
geom_segment(aes(x = 0.61, xend = 0.8, y = 1.45, yend = 1.45), linetype = "dashed", color = "#F2A800") +
geom_segment(aes(x = 0.6, xend = 0.6, y = 1.315, yend = 2.05), linetype = "dashed", color = "#F88600") +
geom_segment(aes(x = 0, xend = 0.6, y = 1.315, yend = 1.3125), linetype = "dashed", color = "#F88600") +
geom_segment(aes(x = 0.12, xend = 0.605, y = 0.9, yend = 0.9), linetype = "dashed", color = "#5BBCD6") +
geom_segment(aes(x = 0.605, xend = 0.605, y = 0.9, yend = 2.05), linetype = "dashed", color = "#5BBCD6") +
geom_segment(aes(x = 0.12, xend = 0.12, y = 0.9, yend = 2.05), linetype = "dashed", color = "#5BBCD6") +
geom_segment(aes(x = 0.605, xend = 0.8, y = 1.2, yend = 1.2), linetype = "dashed", color = "#5BBCD6") +
geom_segment(aes(x = 0.605, xend = 0.605, y = 1.2, yend = 2.05), linetype = "dashed", color = "#5BBCD6") +
geom_point(data = known_molecules, aes(x = O_C_ratio, y = H_C_ratio, shape = color_code_plot), size = 4) +
geom_point(data = this_study, aes(x = O_C_ratio, y = H_C_ratio, fill = assignment_err_ppm_freestyle), size = 2.5, shape = 21, alpha = 1) +
scale_shape_manual(values = c("Known Cu Ligand (Babcock-Adams 2022)" = 24,
"Heme or Heme Catabolite" = 23,
"Dinoflagellate Luciferin" = 4,
"Chl, Phycobilins, and Phyllobilins" = 22,
"Heme/Chl Precursors" = 25,
"Other Metabolites" = 3,
"Known Cu Ligand (Boiteau 2016)" = 8),
labels = c("Chl, Phycobilins,\nand Phyllobilins",
"Dinoflagellate\nLuciferin",
"Heme or Heme\nCatabolite",
"Heme/Chl\nPrecursors",
"Known Cu Ligand\n(Babcock-Adams 2022)",
"Known Cu Ligand\n(Boiteau 2016)",
"Other Metabolites\n(e.g. Coenzyme F430)")) +
scale_fill_gradient(low = "deeppink", high = "chartreuse", limits = c(0,1), guide = guide_colorbar(frame.colour = "black", ticks.colour = "black")) +
geom_text(aes(x = 0.01, y = 0.775, label = "Phytochemical"), color = "#C9211D", fontface = "bold", hjust = 0, vjust = 0) +
geom_text(aes(x = 0.8-0.01, y = 1+(0.775-0.75), label = "Nucleotides"), color = "#288674", fontface = "bold", hjust = 1, vjust = 0) +
geom_text(aes(x = 0.8-0.01, y = 1.45+(0.775-0.75), label = "Amino Sugars"), color = "#F2A800", fontface = "bold", hjust = 1, vjust = 0) +
geom_text(aes(x = 0.01, y = 2.05-(0.775-0.75), label = "Lipid"), color = "#F88600", fontface = "bold", hjust = 0, vjust = 1) +
geom_text(aes(x = 0.605-0.01, y = 2.05-(0.775-0.75), label = "Protein 1"), color = "#5BBCD6", fontface = "bold", hjust = 1, vjust = 1) +
geom_text(aes(x = 0.605+0.01, y = 2.05-(0.775-0.75), label = "Protein 2"), color = "#5BBCD6", fontface = "bold", hjust = 0, vjust = 1) +
labs(x = "O/C", y = "H/C", shape = "Known Tetrapyrroles\nor Cu Ligands", fill = "Assignment Error (ppm)\nWhale Cu Ligands") +
scale_x_continuous(limits = c(0,0.8), expand = c(0,0), breaks = c(0,0.2,0.4,0.6,0.8)) +
scale_y_continuous(limits = c(0.75, 2.05), expand = c(0,0)) +
coord_cartesian(clip = "off") +
theme_classic2(base_size = 17) +
theme(legend.position = "right",
axis.title.x = element_text(face = "bold", color = "black"),
axis.title.y.left = element_text(face = "bold", color = "black"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.text = element_text(face = "bold", color = "black"),
legend.title = element_text(face = "bold", color = "black"),
legend.key.height = unit(1.25,"cm"))
#N/C vs H/C
vk_b = ggplot() +
geom_rect(aes(xmin = 0.025, xmax = 0.275, ymin = 0.75, ymax = 2.05), fill = "white") +
geom_rect(aes(xmin = 0.025, xmax = 0.126, ymin = 0.75, ymax = 1.31), fill = "#C9211D", alpha = 0.3) +
geom_rect(aes(xmin = 0.2, xmax = 0.275, ymin = 1, ymax = 1.8), fill = "#288674", alpha = 0.3) +
geom_rect(aes(xmin = 0.07, xmax = 0.2, ymin = 1.45, ymax = 2.05), fill = "#F2A800", alpha = 0.3) +
geom_rect(aes(xmin = 0.025, xmax = 0.126, ymin = 1.31, ymax = 2.05), fill = "#F88600", alpha = 0.3) +
geom_rect(aes(xmin = 0.126, xmax = 0.275, ymin = 0.9, ymax = 2.05), fill = "#5BBCD6", alpha = 0.3) +
geom_rect(aes(xmin = 0.2, xmax = 0.275, ymin = 1.2, ymax = 2.05), fill = "#5BBCD6", alpha = 0.3) +
geom_segment(aes(x = 0.126, xend = 0.126, y = 0.75, yend = 1.3075), linetype = "dashed", color = "#C9211D") +
geom_segment(aes(x = 0.025, xend = 0.126, y = 1.305, yend = 1.3075), linetype = "dashed", color = "#C9211D") +
geom_segment(aes(x = 0.2, xend = 0.2, y = 1, yend = 1.8), linetype = "dashed", color = "#288674") +
geom_segment(aes(x = 0.2, xend = 0.275, y = 1, yend = 1), linetype = "dashed", color = "#288674") +
geom_segment(aes(x = 0.2, xend = 0.275, y = 1.8, yend = 1.8), linetype = "dashed", color = "#288674") +
geom_segment(aes(x = 0.07, xend = 0.07, y = 1.45, yend = 2.05), linetype = "dashed", color = "#F2A800") +
geom_segment(aes(x = 0.07, xend = 0.19875, y = 1.45, yend = 1.45), linetype = "dashed", color = "#F2A800") +
geom_segment(aes(x = 0.19875, xend = 0.19875, y = 1.45, yend = 2.05), linetype = "dashed", color = "#F2A800") +
geom_segment(aes(x = 0.126, xend = 0.126, y = 1.315, yend = 2.05), linetype = "dashed", color = "#F88600") +
geom_segment(aes(x = 0.025, xend = 0.126, y = 1.315, yend = 1.3125), linetype = "dashed", color = "#F88600") +
geom_segment(aes(x = 0.126, xend = 0.275, y = 0.9, yend = 0.9), linetype = "dashed", color = "#5BBCD6") +
geom_segment(aes(x = 0.126, xend = 0.126, y = 0.9, yend = 2.05), linetype = "dashed", color = "#5BBCD6") +
geom_segment(aes(x = 0.20125, xend = 0.275, y = 1.2, yend = 1.2), linetype = "dashed", color = "#5BBCD6") +
geom_segment(aes(x = 0.20125, xend = 0.20125, y = 1.2, yend = 2.05), linetype = "dashed", color = "#5BBCD6") +
geom_point(data = known_molecules, aes(x = N_C_ratio, y = H_C_ratio, shape = color_code_plot), size = 4) +
geom_point(data = this_study, aes(x = N_C_ratio, y = H_C_ratio, fill = assignment_err_ppm_freestyle), size = 2.5, shape = 21, alpha = 1) +
scale_shape_manual(values = c("Known Cu Ligand (Babcock-Adams 2022)" = 24,
"Heme or Heme Catabolite" = 23,
"Dinoflagellate Luciferin" = 4,
"Chl, Phycobilins, and Phyllobilins" = 22,
"Heme/Chl Precursors" = 25,
"Other Metabolites" = 3,
"Known Cu Ligand (Boiteau 2016)" = 8),
labels = c("Chl, Phycobilins,\nand Phyllobilins",
"Dinoflagellate\nLuciferin",
"Heme or Heme\nCatabolite",
"Heme/Chl\nPrecursors",
"Known Cu Ligand\n(Babcock-Adams 2022)",
"Known Cu Ligand\n(Boiteau 2016)",
"Other Metabolites\n(e.g. Coenzyme F430)")) +
scale_fill_gradient(low = "deeppink", high = "chartreuse", limits = c(0,1), guide = guide_colorbar(frame.colour = "black", ticks.colour = "black")) +
geom_text(aes(x = 0.03, y = 0.775, label = "Phytochemical"), color = "#C9211D", fontface = "bold", hjust = 0, vjust = 0) +
geom_text(aes(x = 0.275-0.005, y = 1+(0.775-0.75), label = "Nucleotides"), color = "#288674", fontface = "bold", hjust = 1, vjust = 0) +
geom_text(aes(x = 0.07+0.0025, y = 2.05-(0.775-0.75), label = "Amino Sugars"), color = "#F2A800", fontface = "bold", hjust = 0, vjust = 1) +
geom_text(aes(x = 0.03, y = 2.05-(0.775-0.75), label = "Lipid"), color = "#F88600", fontface = "bold", hjust = 0, vjust = 1) +
geom_text(aes(x = 0.2-0.005, y = 2.05-(0.775-0.75), label = "Protein 1"), color = "#5BBCD6", fontface = "bold", hjust = 1, vjust = 1) +
geom_text(aes(x = 0.2+0.005, y = 2.05-(0.775-0.75), label = "Protein 2"), color = "#5BBCD6", fontface = "bold", hjust = 0, vjust = 1) +
labs(x = "N/C", y = "H/C", shape = "Known Tetrapyrroles\nor Cu Ligands", fill = "Assignment Error (ppm)\nWhale Cu Ligands") +
scale_x_continuous(limits = c(0.025,0.275), expand = c(0,0), breaks = c(.05,.1,.15,.2,.25)) +
scale_y_continuous(limits = c(0.75, 2.05), expand = c(0,0)) +
coord_cartesian(clip = "off") +
theme_classic2(base_size = 17) +
theme(legend.position = "right",
axis.title.x = element_text(face = "bold", color = "black"),
axis.title.y.left = element_text(face = "bold", color = "black"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.text = element_text(face = "bold", color = "black"),
legend.title = element_text(face = "bold", color = "black"),
legend.key.height = unit(1.25,"cm"))
# combine plots
legend_a <- cowplot::get_legend(vk_a)
VK <- plot_grid(
plot_grid(vk_a + theme(legend.position = "none"),
vk_b + theme(legend.position = "none"),
ncol = 1,
label_size = 17,
labels = c("(A)", "(B)"),
align = "h", hjust = 0.05),
legend_a, rel_widths = c(2, 1)
)
VK
Figure 4
Lability leach results of MN19 and MN20 compared to published bulk particulate data
particle_frame = compare %>% filter(element %in% c("pFe","pCu","Bulk Cu:P","Bulk Fe:P","Fe:P","Cu:P","Bulk Fe","Bulk Cu") & !(species_samp %in% c("CRC7","CRC210","MSS1","Sperm","Pygmy Blue","Fin")))
particle_frame$species_samp = factor(particle_frame$species_samp, levels = c("MN19","MN20","Blue","Humpback","Average"))
particle_frame$element = factor(particle_frame$element, levels = c("pFe","Fe:P","pCu","Cu:P","Bulk Fe","Bulk Fe:P","Bulk Cu","Bulk Cu:P"))
library(ggtext)
ggplot(particle_frame) +
geom_col(aes(x = species_samp,
y = value_umol_kg,
fill = element),
width = 0.8,
position = position_dodge(0.8),
color = "black") +
geom_errorbar(data = particle_frame %>% filter(element == "pFe" | element == "Bulk Fe"),
aes(x = species_samp,
ymin = value_umol_kg - stddev_umol_kg,
ymax = value_umol_kg + stddev_umol_kg),
width = 0.09,
size = 0.7,
position = position_nudge(x = -0.3)) +
geom_errorbar(data = particle_frame %>% filter(element == "pCu" | element == "Bulk Cu"),
aes(x = species_samp,
ymin = value_umol_kg - stddev_umol_kg,
ymax = value_umol_kg + stddev_umol_kg),
width = 0.09,
size = 0.7,
position = position_nudge(x = (0.0975))) +
geom_errorbar(data = particle_frame %>% filter(element == "Fe:P" | element == "Bulk Fe:P"),
aes(x = species_samp,
ymin = value_umol_kg - stddev_umol_kg,
ymax = value_umol_kg + stddev_umol_kg),
width = 0.09,
size = 0.7,
position = position_nudge(x = -(0.0975))) +
geom_errorbar(data = particle_frame %>% filter(element == "Cu:P" | element == "Bulk Cu:P"),
aes(x = species_samp,
ymin = value_umol_kg - stddev_umol_kg,
ymax = value_umol_kg + stddev_umol_kg),
width = 0.09,
size = 0.7,
position = position_nudge(x = 0.3)) +
labs(x = "",
#y = "<b><span style = 'color:#DD8D29;'>[pFe]<sub></sub> (µmol/kg)</span><span style = 'color:#000000;'>, </span><span style = 'color:#00A08A;'>[pCu]<sub></sub> (µmol/kg)</span><br><span style = 'color:#BE2819;'>[Fe:P]<sub></sub> (µmol/mol)</span><span style = 'color:#000000;'>, </span><span style = 'color:#02401B;'>[Cu:P]<sub></sub> (µmol/mol)</span></b>") +
y = "") +
scale_y_continuous(lim = 1000*c(0,8),
breaks = 1000*c(1,2,3,4,5,6,7,8),
sec.axis = sec_axis(~ . * 1,
#name = "<b><span style = 'color:#DD8D29;'>[pFe]<sub></sub> (mmol/kg)</span><span style = 'color:#000000;'>, </span><span style = 'color:#00A08A;'>[pCu]<sub></sub> (mmol/kg)</span><br><span style = 'color:#BE2819;'>[Fe:P]<sub></sub> (mmol/mol)</span><span style = 'color:#000000;'>, </span><span style = 'color:#02401B;'>[Cu:P]<sub></sub> (mmol/mol)</span></b>",
breaks = 1000*c(1,2,3,4,5,6,7,8))) +
scale_x_discrete(labels = c("MN19","MN20","Blue\n(n = 15)","Humpback\n(n = 2)","Average\nBaleen\n(n = 26)")) +
scale_fill_manual(values = c("#DD8D29","#BE2819","#00A08A","#02401B","#DD8D29","#BE2819","#00A08A","#02401B")) +
geom_vline(xintercept = 2.5, linetype = "dashed", color = "black", size = 1) +
geom_text(label = "Lability\nLeach\n(This Study)",
x = 1.5,
y = 7250,
size = 5,
fontface = "bold") +
geom_text(label = "Total Digestion\n(Nicol et al., 2010)\n(Ratnarajah et al., 2014)",
x = 4,
y = 7250,
size = 5,
fontface = "bold") +
geom_text(label = "[pFe] (µmol/kg)",
x = 1-0.31,
y = (particle_frame %>% filter(species_samp == "MN19" & element == "pFe"))$value_umol_kg + 225,
size = 5,
angle = 90,
hjust = 0,
color = "#DD8D29",
fontface = "bold") +
geom_text(label = "[pCu] (µmol/kg)",
x = 1+0.095,
y = (particle_frame %>% filter(species_samp == "MN19" & element == "pCu"))$value_umol_kg + 225,
size = 5,
angle = 90,
hjust = 0,
color = "#00A08A",
fontface = "bold") +
geom_text(label = "[Fe:P] (µmol Fe/mol P)",
x = 1-0.114,
y = (particle_frame %>% filter(species_samp == "MN19" & element == "Fe:P"))$value_umol_kg + 225,
size = 5,
angle = 90,
hjust = 0,
color = "#BE2819",
fontface = "bold") +
geom_text(label = "[Cu:P] (µmol Cu/mol P)",
x = 1+0.29,
y = (particle_frame %>% filter(species_samp == "MN19" & element == "Cu:P"))$value_umol_kg + 225,
size = 5,
angle = 90,
hjust = 0,
color = "#02401B",
fontface = "bold") +
coord_cartesian(expand = FALSE) +
theme_classic2(base_size = 17.5) +
theme(axis.title.x = element_blank(),
axis.title.y = element_markdown(),
axis.text.x = element_text(angle = 0, face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
axis.title.y.right = element_markdown(),
legend.position = "none",
axis.text.y.right = element_text(face = "bold", color = "black"))
Figure 5
Figure S1
Example iron chromatogram for sample MN19
#Replace this with your element of choice, options right now are what's in the dataframe above
#You can adjust if you want, but right now it's 56Fe, 63Cu, 60Ni, 55Mn
element_to_analyze = "56Fe"
ICPdat_onesamp_onelement <- ICPdata %>%
filter(element == element_to_analyze) %>%
filter(time > 300) # excluding first 5 min before LC pump switches from loading pump to nano pumps
ICPdat_onesamp_onelement.t <- t(ICPdat_onesamp_onelement$intensity) %>% as.matrix()
lambda_try = 8
bc.als <- baseline(ICPdat_onesamp_onelement.t,
lambda = lambda_try, p = .01, maxit = 20, method='als')
# This smooths out youre ICPMS data corrected with the baseline you chose
ICPdat_onesamp_onelement_wbl <- ICPdat_onesamp_onelement %>%
mutate(intensity_baselined = c(getBaseline(bc.als)),
intensity_corrected = c(getCorrected(bc.als))) %>%
mutate(intensity_smoothed = sgolayfilt(intensity, p = 15),
intensity_corrected_smoothed = sgolayfilt(intensity_corrected, p = 15))
ICP<-cbind(ICPdat_onesamp_onelement_wbl[,c("time")],
ICPdat_onesamp_onelement_wbl[,c("intensity_corrected_smoothed")])
colnames(ICP) = c("RT","Int")
ICP_smoothish<-ksmooth(ICP[,1],ICP[,2],kernel="normal",bandwidth=10)
fe_icpms_graph_smooth = ggplot() +
geom_line(data = as.data.frame(ICP_smoothish),
aes(x = x/60, y = y), color = "black", size = 1) +
scale_x_continuous(limits = c(5,35), breaks = c(5,10,15,20,25,30,35)) +
scale_y_continuous(labels = scales::scientific) +
labs(x = "Retention Time (min)",
y = expression(bold(paste(""^"56","Fe", " Intensity (cps)")))) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 18) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"))
fe_icpms_graph_smooth
Figure S2
Voltammograms from Fe ligand analyses with CLE-ACSV
g1 = ggplot(data = MN19 %>% filter(treatment != "SPIKE")) +
aes(x = potential, y = Y, color = treatment) +
geom_line(size = 1) +
scale_color_manual(values = stepped3(16)[16:2], labels = c("0 nM",
"0 nM",
"1 nM",
"2 nM",
"3 nM",
"5 nM",
"7.5 nM",
"10 nM",
"15 nM",
"20 nM",
"30 nM",
"40 nM",
"50 nM",
"75 nM",
"100 nM",
"5 nM spike")) +
labs(title = "MN19", x = "Potential (V)", y = "Current (A)", color = "Fe Added") +
scale_x_reverse() +
guides(color=guide_legend(ncol=4)) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 19) +
theme(legend.position=c(1,0.995),
legend.justification = c("right","top"),
axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.title = element_text(face = "bold"),
legend.text = element_text(face = "bold"))
g2 = ggplot(data = MN20 %>% filter(treatment != "SPIKE")) +
aes(x = potential, y = Y, color = treatment) +
geom_line(size = 1) +
scale_color_manual(values = stepped3(16)[16:2], labels = c("0 nM",
"0 nM",
"0.5 nM",
"1.5 nM",
"2 nM",
"3 nM",
"4 nM",
"5 nM",
"7.5 nM",
"10 nM",
"15 nM",
"20 nM",
"30 nM",
"40 nM",
"50 nM",
"5 nM spike")) +
labs(title = "MN20", x = "Potential (V)", y = "Current (A)", color = "Fe Added") +
scale_x_reverse() +
guides(color=guide_legend(ncol=4)) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 19) +
theme(legend.position=c(1,0.995),
legend.justification = c("right","top"),
axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.title = element_text(face = "bold"),
legend.text = element_text(face = "bold"))
g3 = ggplot(data = MSS1 %>% filter(treatment != "SPIKE")) +
aes(x = potential, y = Y, color = treatment) +
geom_line(size = 1) +
scale_color_manual(values = stepped3(16)[16:2], labels = c("0 nM",
"0 nM",
"1 nM",
"2 nM",
"3 nM",
"5 nM",
"7.5 nM",
"10 nM",
"15 nM",
"20 nM",
"30 nM",
"40 nM",
"50 nM",
"75 nM",
"100 nM",
"5 nM spike")) +
labs(title = "MSS1", x = "Potential (V)", y = "Current (A)", color = "Fe Added") +
scale_x_reverse() +
guides(color=guide_legend(ncol=4)) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 19) +
theme(legend.position=c(1,0.995),
legend.justification = c("right","top"),
axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.title = element_text(face = "bold"),
legend.text = element_text(face = "bold"))
g4 = ggplot(data = CRC7 %>% filter(treatment != "SPIKE")) +
aes(x = potential, y = Y, color = treatment) +
geom_line(size = 1) +
scale_color_manual(values = stepped3(16)[16:2], labels = c("0 nM",
"0 nM",
"0.5 nM",
"1.5 nM",
"2 nM",
"3 nM",
"4 nM",
"5 nM",
"7.5 nM",
"10 nM",
"15 nM",
"20 nM",
"30 nM",
"40 nM",
"50 nM",
"5 nM spike")) +
labs(title = "CRC7", x = "Potential (V)", y = "Current (A)", color = "Fe Added") +
scale_x_reverse() +
guides(color=guide_legend(ncol=4)) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 19) +
theme(legend.position=c(1,0.995),
legend.justification = c("right","top"),
axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.title = element_text(face = "bold"),
legend.text = element_text(face = "bold"))
g5 = ggplot(data = CRC210 %>% filter(treatment != "SPIKE")) +
aes(x = potential, y = Y, color = treatment) +
geom_line(size = 1) +
scale_color_manual(values = stepped3(16)[16:2], labels = c("0 nM",
"0 nM",
"1 nM",
"2 nM",
"3 nM",
"5 nM",
"7.5 nM",
"10 nM",
"15 nM",
"20 nM",
"30 nM",
"40 nM",
"50 nM",
"75 nM",
"100 nM",
"5 nM spike")) +
labs(title = "CRC210", x = "Potential (V)", y = "Current (A)", color = "Fe Added") +
scale_x_reverse() +
guides(color=guide_legend(ncol=4)) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 19) +
theme(legend.position=c(1,0.995),
legend.justification = c("right","top"),
axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.title = element_text(face = "bold"),
legend.text = element_text(face = "bold"))
g6 = NULL
library(cowplot)
CSV_plot = plot_grid(g1 + theme(legend.title = element_text(size = 13.5),
legend.text = element_text(size = 13.5)),
g2 + theme(legend.title = element_text(size = 13.5),
legend.text = element_text(size = 13.5)),
g3 + theme(legend.title = element_text(size = 13.5),
legend.text = element_text(size = 13.5),
legend.background = element_rect(color = "transparent",fill = "transparent")),
g4 + theme(legend.title = element_text(size = 13.5),
legend.text = element_text(size = 13.5),
legend.background = element_rect(color = "transparent",fill = "transparent")),
g5 + theme(legend.title = element_text(size = 13.5),
legend.text = element_text(size = 13.5)),
g6,
nrow = 3,
labels = c("(A)","(B)","(C)","(D)","(E)",""),
align = "hv",
label_size = 23)
CSV_plot
Figure S3
Voltammograms from standard addition of humic acid standard
g1 = ggplot(data = MN19_eHS) +
aes(x = potential, y = Y, color = treatment) +
geom_line(size = 1) +
scale_color_manual(values = brewer.pal(9, "RdPu")[2:9], labels = c("0 µg/L",
"0 µg/L",
"0 µg/L",
"25 µg/L",
"50 µg/L",
"100 µg/L",
"150 µg/L",
"300 µg/L")) +
labs(title = "MN19", x = "Potential (V)", y = "Current (A)", color = "SRHA Added") +
scale_x_reverse() +
guides(color=guide_legend(ncol=2)) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 19) +
theme(legend.position=c(1,0.005),
legend.justification = c("right","bottom"),
axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.title = element_text(face = "bold", size = 13.5),
legend.text = element_text(face = "bold", size = 13.5))
g2 = ggplot(data = MN20_eHS) +
aes(x = potential, y = Y, color = treatment) +
geom_line(size = 1) +
scale_color_manual(values = brewer.pal(9, "RdPu")[2:9], labels = c("0 µg/L",
"0 µg/L",
"0 µg/L",
"25 µg/L",
"50 µg/L",
"100 µg/L",
"150 µg/L",
"300 µg/L")) +
labs(title = "MN20", x = "Potential (V)", y = "Current (A)", color = "SRHA Added") +
scale_x_reverse() +
guides(color=guide_legend(ncol=2)) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 19) +
theme(legend.position=c(1,0.005),
legend.justification = c("right","bottom"),
axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.title = element_text(face = "bold", size = 13.5),
legend.text = element_text(face = "bold", size = 13.5))
g3 = ggplot(data = MSS1_eHS) +
aes(x = potential, y = Y, color = treatment) +
geom_line(size = 1) +
scale_color_manual(values = brewer.pal(9, "RdPu")[2:9], labels = c("0 µg/L",
"0 µg/L",
"0 µg/L",
"25 µg/L",
"50 µg/L",
"100 µg/L",
"150 µg/L",
"225 µg/L")) +
labs(title = "MSS1", x = "Potential (V)", y = "Current (A)", color = "SRHA Added") +
scale_x_reverse() +
guides(color=guide_legend(ncol=2)) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 19) +
theme(legend.position=c(1,0.005),
legend.justification = c("right","bottom"),
axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.title = element_text(face = "bold", size = 13.5),
legend.text = element_text(face = "bold", size = 13.5))
g4 = ggplot(data = CRC7_eHS) +
aes(x = potential, y = Y, color = treatment) +
geom_line(size = 1) +
scale_color_manual(values = brewer.pal(9, "RdPu")[2:9], labels = c("0 µg/L",
"0 µg/L",
"0 µg/L",
"25 µg/L",
"50 µg/L",
"100 µg/L",
"150 µg/L",
"225 µg/L")) +
labs(title = "CRC7", x = "Potential (V)", y = "Current (A)", color = "SRHA Added") +
scale_x_reverse() +
guides(color=guide_legend(ncol=2)) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 19) +
theme(legend.position=c(1,0.005),
legend.justification = c("right","bottom"),
axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.title = element_text(face = "bold", size = 13.5),
legend.text = element_text(face = "bold", size = 13.5))
g5 = ggplot(data = CRC210_eHS) +
aes(x = potential, y = Y, color = treatment) +
geom_line(size = 1) +
scale_color_manual(values = brewer.pal(9, "RdPu")[2:9], labels = c("0 µg/L",
"0 µg/L",
"0 µg/L",
"25 µg/L",
"50 µg/L",
"100 µg/L",
"150 µg/L",
"225 µg/L")) +
labs(title = "CRC210", x = "Potential (V)", y = "Current (A)", color = "SRHA Added") +
scale_x_reverse() +
guides(color=guide_legend(ncol=2)) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 19) +
theme(legend.position=c(1,0.005),
legend.justification = c("right","bottom"),
axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.title = element_text(face = "bold", size = 13.5),
legend.text = element_text(face = "bold", size = 13.5))
g6 = NULL
library(cowplot)
humic_CSV_plot = plot_grid(g1, g2, g3, g4, g5, g6,
nrow = 3,
labels = c("(A)","(B)","(C)","(D)","(E)",""),
align = "hv",
label_size = 23)
humic_CSV_plot
Figure S4
Extracted ion chromatograms (EICs) of domoic acid ion
DA_mass = 312.144714
DA_mass_Fe = 365.056178
DA_mass_Cu = 373.05866299999997
DA_mass_Na = 334.126659
DA_mass_NH4 = 329.171263
DA_trace = ESIrams$MS1[mz %between% pmppm(DA_mass, 5)]
DA_trace_Fe = ESIrams$MS1[mz %between% pmppm(DA_mass_Fe, 5)]
DA_trace_Cu = ESIrams$MS1[mz %between% pmppm(DA_mass_Cu, 5)]
DA_trace_Na = ESIrams$MS1[mz %between% pmppm(DA_mass_Na, 5)]
DA_trace_NH4 = ESIrams$MS1[mz %between% pmppm(DA_mass_NH4, 5)]
normal_plot = ggplot(DA_trace) +
geom_segment(aes(x = rt, y = 0, xend = rt, yend = int)) +
scale_x_continuous(expand = c(0,0), limits = c(0,40), breaks = c(5,10,15,20,25,30,35)) +
scale_y_continuous(expand = c(0,0), limits = c(0,2e5), labels = scientific) +
labs(x = "Retention Time (min)",
y = "LC-ESI-MS (Orbitrap)\nIntensity") +
geom_text(aes(label = paste0("m/z range: ",round(pmppm(DA_mass,5)[1], 4),"–",round(pmppm(DA_mass,5)[2], 4)), x = 15, y = 1.5e5), size = 6) +
geom_text(aes(label = "Domoic Acid + H", x = 15, y = 1.3e5), size = 6) +
theme_classic2(base_size = 18) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
axis.text.x = element_text(face = "bold"),
axis.text.y.left = element_text(face = "bold"))
Fe_plot = ggplot(DA_trace_Fe) +
geom_segment(aes(x = rt, y = 0, xend = rt, yend = int)) +
scale_x_continuous(expand = c(0,0), limits = c(0,40), breaks = c(5,10,15,20,25,30,35)) +
scale_y_continuous(expand = c(0,0), limits = c(0,2e5), labels = scientific) +
labs(x = "Retention Time (min)",
y = "LC-ESI-MS (Orbitrap)\nIntensity") +
geom_text(aes(label = paste0("m/z range: ",round(pmppm(DA_mass_Fe,5)[1], 4),"–",round(pmppm(DA_mass_Fe,5)[2], 4)), x = 15, y = 1.5e5), size = 6) +
geom_text(aes(label = "Domoic Acid + Fe - 2H", x = 15, y = 1.3e5), size = 6) +
theme_classic2(base_size = 18) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
axis.text.x = element_text(face = "bold"),
axis.text.y.left = element_text(face = "bold"))
Cu_plot = ggplot(DA_trace_Cu) +
geom_segment(aes(x = rt, y = 0, xend = rt, yend = int)) +
scale_x_continuous(expand = c(0,0), limits = c(0,40), breaks = c(5,10,15,20,25,30,35)) +
scale_y_continuous(expand = c(0,0), limits = c(0,2e5), labels = scientific) +
labs(x = "Retention Time (min)",
y = "LC-ESI-MS (Orbitrap)\nIntensity") +
geom_text(aes(label = paste0("m/z range: ",round(pmppm(DA_mass_Cu,5)[1], 4),"–",round(pmppm(DA_mass_Cu,5)[2], 4)), x = 15, y = 1.5e5), size = 6) +
geom_text(aes(label = "Domoic Acid + Cu - H", x = 15, y = 1.3e5), size = 6) +
theme_classic2(base_size = 18) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
axis.text.x = element_text(face = "bold"),
axis.text.y.left = element_text(face = "bold"))
# ggplot(DA_trace_Na) +
# geom_segment(aes(x = rt, y = 0, xend = rt, yend = int)) +
# scale_x_continuous(expand = c(0,0), limits = c(0,40), breaks = c(5,10,15,20,25,30,35)) +
# scale_y_continuous(expand = c(0,0), limits = c(0,2e5), labels = scientific) +
# labs(x = "Retention Time (min)",
# y = "LC-ESI-MS (Orbitrap)\nIntensity") +
# geom_text(aes(label = paste0("m/z range: ",round(pmppm(DA_mass_Na,5)[1], 4),"–",round(pmppm(DA_mass_Na,5)[2], 4)), x = 15, y = 1.5e5), size = 6) +
# geom_text(aes(label = "Domoic Acid + Na", x = 15, y = 1.3e5), size = 6) +
# theme_classic2(base_size = 18) +
# theme(axis.title.x = element_text(face = "bold"),
# axis.title.y.left = element_text(face = "bold"),
# axis.text.x = element_text(face = "bold"),
# axis.text.y.left = element_text(face = "bold"))
#
# ggplot(DA_trace_NH4) +
# geom_segment(aes(x = rt, y = 0, xend = rt, yend = int)) +
# scale_x_continuous(expand = c(0,0), limits = c(0,40), breaks = c(5,10,15,20,25,30,35)) +
# scale_y_continuous(expand = c(0,0), limits = c(0,2e5), labels = scientific) +
# labs(x = "Retention Time (min)",
# y = "LC-ESI-MS (Orbitrap)\nIntensity") +
# geom_text(aes(label = paste0("m/z range: ",round(pmppm(DA_mass_NH4,5)[1], 4),"–",round(pmppm(DA_mass_NH4,5)[2], 4)), x = 15, y = 1.5e5), size = 6) +
# geom_text(aes(label = "Domoic Acid + NH4", x = 15, y = 1.3e5), size = 6) +
# theme_classic2(base_size = 18) +
# theme(axis.title.x = element_text(face = "bold"),
# axis.title.y.left = element_text(face = "bold"),
# axis.text.x = element_text(face = "bold"),
# axis.text.y.left = element_text(face = "bold"))
supp_fig_DA = plot_grid(normal_plot, Fe_plot, Cu_plot,
ncol = 1,
labels = c("(A)","(B)","(C)"),
label_size = 20,
align = "hv")
supp_fig_DA
Figure S5
Figure S5 was a photograph of sample filtrate, with no code.
Figure S6
MS2 spectra from select candidate Cu ligands in sample MN19. Further annotations of this figure was conducted on ChemDraw.
mz_ms2_1 = 650.21622
mz_rt_1 = 26.46
apo_ms2_1 = 589.30212
apo_rt_1 = 24.41
mz_ms2_2 = 652.23196
mz_rt_2 = 28.25
apo_ms2_2 = 591.31788
apo_rt_2 = 24.47
mz_ms2_3 = 654.24755
mz_rt_3 = 25.59
apo_ms2_3 = 593.33348
apo_rt_3 = 23.46
mz_ms2_4 = 684.20469
mz_rt_4 = 26.4
apo_ms2_4 = 623.28991
apo_rt_4 = 25.16
mz_rams_1 = ESIrams_FT$MS2[premz%between%pmppm(mz_ms2_1,50) & rt %between% c(mz_rt_1 - 2, mz_rt_1 + 2)] %>% arrange(int)
mz_rams_1$int = mz_rams_1$int/max(mz_rams_1$int)
top_peaks_mz_1 <- (mz_rams_1 %>%
top_n(10, int) %>%
arrange(desc(int)))[c(1,3,4,5,8),]
a = ggplot(mz_rams_1) +
geom_segment(aes(x=fragmz, xend=fragmz, y=int, yend=0), #color = as.character(round(rt, 2))),
) +
scale_y_continuous(limits = c(0,1.075),
expand = c(0,0),
breaks = c(0, .25, .5, .75, 1),
labels = c("0%", "25%", "50%", "75%", "100%")) +
scale_x_continuous(limits = c(100,700), expand = c(0,0),
breaks = c(200,300,400,500,600)) +
geom_text(data = top_peaks_mz_1[c(1,2),],
aes(x = fragmz, y = int + 0.05, label = round(fragmz, 2)), hjust = 0.5) +
geom_text(data = top_peaks_mz_1[c(3),],
aes(x = fragmz + 5, y = int + 0.11, label = round(fragmz, 2)), hjust = 0) +
geom_text(data = top_peaks_mz_1[c(4),],
aes(x = fragmz + 5, y = int + 0.04, label = round(fragmz, 2)), hjust = 0) +
geom_text(data = top_peaks_mz_1[c(5),],
aes(x = fragmz - 7.5, y = int + 0.05, label = round(fragmz, 2)), hjust = 0.5) +
coord_cartesian(clip = "off") +
labs(x = "Fragment m/z",
y = "Relative Intensity",
#color = "",
title = bquote(bold(.("[C"))[bold(.("33"))]*bold(.("H"))[bold(.("39"))]*bold(.("O"))[bold(.("6"))]*bold(.("N"))[bold(.("4"))]*bold(.("Cu]"))^bold(.("+"))),
subtitle = paste0("Precursor m/z = ",round(min(mz_rams_1$premz),3), ", RT = ",round(min(mz_rams_1$rt),2),"-", round(max(mz_rams_1$rt),2)," min")) +
theme_classic2(base_size = 12) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.subtitle = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"))
b = ggplot(mz_rams_1) +
geom_segment(aes(x=fragmz, xend=fragmz, y=int, yend=0), alpha = 0) +
scale_y_continuous(limits = c(0,1.075),
expand = c(0,0),
breaks = c(0, .25, .5, .75, 1),
labels = c("0%", "25%", "50%", "75%", "100%")) +
scale_x_continuous(limits = c(100,700), expand = c(0,0),
breaks = c(200,300,400,500,600)) +
coord_cartesian(clip = "off") +
labs(x = "Fragment m/z",
y = "Relative Intensity",
#color = "",
title = bquote(bold(.("[C"))[bold(.("33"))]*bold(.("H"))[bold(.("41"))]*bold(.("O"))[bold(.("6"))]*bold(.("N"))[bold(.("4"))]*bold(.("]"))^bold(.("+"))),
subtitle = paste0("No MS2 spectra collected")) +
theme_classic2(base_size = 12) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.subtitle = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"))
mz_rams_2 = ESIrams_FT$MS2[premz%between%pmppm(mz_ms2_2,100) & rt %between% c(mz_rt_2 - 2, mz_rt_2 + 2)] %>% arrange(int)
mz_rams_2$int = mz_rams_2$int/max(mz_rams_2$int)
top_peaks_mz_2 <- (mz_rams_2 %>%
top_n(10, int) %>%
arrange(desc(int)))[c(1,2,4,6,8),]
c = ggplot(mz_rams_2) +
geom_segment(aes(x=fragmz, xend=fragmz, y=int, yend=0), #color = as.character(round(rt, 2))),
) +
scale_y_continuous(limits = c(0,1.075),
expand = c(0,0),
breaks = c(0, .25, .5, .75, 1),
labels = c("0%", "25%", "50%", "75%", "100%")) +
scale_x_continuous(limits = c(100,700), expand = c(0,0),
breaks = c(200,300,400,500,600)) +
geom_text(data = top_peaks_mz_2[c(1,2,4,5),],
aes(x = fragmz, y = int + 0.05, label = round(fragmz, 2)), hjust = 0.5) +
geom_text(data = top_peaks_mz_2[c(3),],
aes(x = fragmz + 5, y = int + 0.05, label = round(fragmz, 2)), hjust = 0) +
coord_cartesian(clip = "off") +
labs(x = "Fragment m/z",
y = "Relative Intensity",
#color = "",
title = bquote(bold(.("[C"))[bold(.("33"))]*bold(.("H"))[bold(.("41"))]*bold(.("O"))[bold(.("6"))]*bold(.("N"))[bold(.("4"))]*bold(.("Cu]"))^bold(.("+"))),
subtitle = paste0("Precursor m/z = ",round(min(mz_rams_2$premz),3), ", RT = ",round(min(mz_rams_2$rt),2),"-", round(max(mz_rams_2$rt),2)," min")) +
theme_classic2(base_size = 12) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.subtitle = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"))
apo_rams_2 = ESIrams_FT$MS2[premz%between%pmppm(apo_ms2_2,50) & rt %between% c(apo_rt_2 - 2, apo_rt_2 + 2)] %>% arrange(int)
apo_rams_2$int = apo_rams_2$int/max(apo_rams_2$int)
top_peaks_apo_2 <- apo_rams_2 %>%
top_n(5, int) %>%
arrange(desc(int))
d = ggplot(apo_rams_2) +
geom_segment(aes(x=fragmz, xend=fragmz, y=int, yend=0), #color = as.character(round(rt, 2))),
) +
scale_y_continuous(limits = c(0,1.1),
expand = c(0,0),
breaks = c(0, .25, .5, .75, 1),
labels = c("0%", "25%", "50%", "75%", "100%")) +
scale_x_continuous(limits = c(100,700), expand = c(0,0),
breaks = c(200,300,400,500,600)) +
geom_text(data = top_peaks_apo_2[c(1:4),],
aes(x = fragmz, y = int + 0.05, label = round(fragmz, 2)), hjust = 0.5) +
geom_text(data = top_peaks_apo_2[c(5),],
aes(x = fragmz, y = int + 0.05, label = round(fragmz, 2)), hjust = 0.1) +
coord_cartesian(clip = "off") +
labs(x = "Fragment m/z",
y = "Relative Intensity",
#color = "",
title = bquote(bold(.("[C"))[bold(.("33"))]*bold(.("H"))[bold(.("43"))]*bold(.("O"))[bold(.("6"))]*bold(.("N"))[bold(.("4"))]*bold(.("]"))^bold(.("+"))),
subtitle = paste0("Precursor m/z = ",round(min(apo_rams_2$premz),3),"-",round(max(apo_rams_2$premz),3), ", RT = ",round(min(apo_rams_2$rt),2),"-", round(max(apo_rams_2$rt),2)," min")) +
theme_classic2(base_size = 12) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.subtitle = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"))
mz_rams_3 = ESIrams_FT$MS2[premz%between%pmppm(mz_ms2_3,200)] %>% arrange(int)
mz_rams_3$int = mz_rams_3$int/max(mz_rams_3$int)
top_peaks_mz_3 <- (mz_rams_3 %>%
top_n(10, int) %>%
arrange(desc(int)))[c(1,3,4,7,8),]
e = ggplot(mz_rams_3) +
geom_segment(aes(x=fragmz, xend=fragmz, y=int, yend=0), #color = as.character(round(rt, 2))),
) +
scale_y_continuous(limits = c(0,1.075),
expand = c(0,0),
breaks = c(0, .25, .5, .75, 1),
labels = c("0%", "25%", "50%", "75%", "100%")) +
scale_x_continuous(limits = c(100,700), expand = c(0,0),
breaks = c(200,300,400,500,600)) +
coord_cartesian(clip = "off") +
geom_text(data = top_peaks_mz_3[c(1,4,5),],
aes(x = fragmz, y = int + 0.05, label = round(fragmz, 2)), hjust = 0.5) +
geom_text(data = top_peaks_mz_3[c(2),],
aes(x = fragmz + 5, y = int + 0.02, label = round(fragmz, 2)), hjust = 0) +
geom_text(data = top_peaks_mz_3[c(3),],
aes(x = fragmz + 5, y = int + 0.12, label = round(fragmz, 2)), hjust = 0) +
labs(x = "Fragment m/z",
y = "Relative Intensity",
#color = "",
title = bquote(bold(.("[C"))[bold(.("33"))]*bold(.("H"))[bold(.("43"))]*bold(.("O"))[bold(.("6"))]*bold(.("N"))[bold(.("4"))]*bold(.("Cu]"))^bold(.("+"))),
subtitle = paste0("Precursor m/z = ",round(min(mz_rams_3$premz),3),"-",round(max(mz_rams_3$premz),3), ", RT = ",round(min(mz_rams_3$rt),2),"-", round(max(mz_rams_3$rt),2)," min")) +
theme_classic2(base_size = 12) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.subtitle = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"))
apo_rams_3 = ESIrams_FT$MS2[premz%between%pmppm(apo_ms2_3,50) & rt %between% c(apo_rt_3 - 2, apo_rt_3 + 2)] %>% arrange(int)
apo_rams_3$int = apo_rams_3$int/max(apo_rams_3$int)
top_peaks_apo_3 <- (apo_rams_3 %>%
top_n(10, int) %>%
arrange(desc(int)))[c(1,2,3,4,5,8),]
f = ggplot(apo_rams_3) +
geom_segment(aes(x=fragmz, xend=fragmz, y=int, yend=0), #color = as.character(round(rt, 2))),
) +
scale_y_continuous(limits = c(0,1.075),
expand = c(0,0),
breaks = c(0, .25, .5, .75, 1),
labels = c("0%", "25%", "50%", "75%", "100%")) +
scale_x_continuous(limits = c(100,700), expand = c(0,0),
breaks = c(200,300,400,500,600)) +
geom_text(data = top_peaks_apo_3[c(1,2,5,6),],
aes(x = fragmz, y = int + 0.05, label = round(fragmz, 2)), hjust = 0.5) +
geom_text(data = top_peaks_apo_3[c(3),],
aes(x = fragmz + 5, y = int + 0.02, label = round(fragmz, 2)), hjust = 0) +
geom_text(data = top_peaks_apo_3[c(4),],
aes(x = fragmz + 5, y = int + 0.12, label = round(fragmz, 2)), hjust = 0) +
coord_cartesian(clip = "off") +
labs(x = "Fragment m/z",
y = "Relative Intensity",
#color = "",
title = bquote(bold(.("[C"))[bold(.("33"))]*bold(.("H"))[bold(.("45"))]*bold(.("O"))[bold(.("6"))]*bold(.("N"))[bold(.("4"))]*bold(.("]"))^bold(.("+"))),
subtitle = paste0("Precursor m/z = ",round(min(apo_rams_3$premz),3), ", RT = ",round(min(apo_rams_3$rt),2)," min")) +
theme_classic2(base_size = 12) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.subtitle = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"))
mz_rams_4 = ESIrams_FT$MS2[premz%between%pmppm(mz_ms2_4,100) & rt %between% c(mz_rt_4 - 2, mz_rt_4 + 2)] %>% arrange(int)
mz_rams_4$int = mz_rams_4$int/max(mz_rams_4$int)
top_peaks_mz_4 <- (mz_rams_4 %>%
top_n(10, int) %>%
arrange(desc(int)))[c(1,4,6,9,10),]
i = ggplot(mz_rams_4) +
geom_segment(aes(x=fragmz, xend=fragmz, y=int, yend=0), #color = as.character(round(rt, 2))),
) +
scale_y_continuous(limits = c(0,1.075),
expand = c(0,0),
breaks = c(0, .25, .5, .75, 1),
labels = c("0%", "25%", "50%", "75%", "100%")) +
scale_x_continuous(limits = c(100,700), expand = c(0,0),
breaks = c(200,300,400,500,600)) +
geom_text(data = top_peaks_mz_4[c(1),],
aes(x = fragmz, y = int + 0.05, label = round(fragmz, 2)), hjust = 0.5) +
geom_text(data = top_peaks_mz_4[c(3),],
aes(x = fragmz, y = int + 0.09, label = round(fragmz, 2)), hjust = 0.5) +
geom_text(data = top_peaks_mz_4[c(2),],
aes(x = fragmz, y = int + 0.05, label = round(fragmz, 2)), hjust = 0.8) +
geom_text(data = top_peaks_mz_4[c(4),],
aes(x = fragmz, y = int + 0.05, label = round(fragmz, 2)), hjust = 0.8) +
geom_text(data = top_peaks_mz_4[c(5),],
aes(x = fragmz, y = int + 0.05, label = round(fragmz, 2)), hjust = 0.3) +
coord_cartesian(clip = "off") +
labs(x = "Fragment m/z",
y = "Relative Intensity",
#color = "",
title = bquote(bold(.("[C"))[bold(.("33"))]*bold(.("H"))[bold(.("41"))]*bold(.("O"))[bold(.("6"))]*bold(.("N"))[bold(.("4"))]*bold(.("SCu]"))^bold(.("+"))),
subtitle = paste0("Precursor m/z = ",round(min(mz_rams_4$premz),3),"-",round(max(mz_rams_4$premz),3), ", RT = ",round(min(mz_rams_4$rt),2),"-", round(max(mz_rams_4$rt),2)," min")) +
theme_classic2(base_size = 12) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.subtitle = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"))
apo_rams_4 = ESIrams_FT$MS2[premz%between%pmppm(apo_ms2_4,50) & rt %between% c(apo_rt_4 - 2, apo_rt_4 + 2)] %>% arrange(int)
apo_rams_4$int = apo_rams_4$int/max(apo_rams_4$int)
top_peaks_apo_4 <- (apo_rams_4 %>%
top_n(10, int) %>%
arrange(desc(int)))[c(1,2,4,5,7),]
j = ggplot(apo_rams_4) +
geom_segment(aes(x=fragmz, xend=fragmz, y=int, yend=0), #color = as.character(round(rt, 2))),
) +
scale_y_continuous(limits = c(0,1.075),
expand = c(0,0),
breaks = c(0, .25, .5, .75, 1),
labels = c("0%", "25%", "50%", "75%", "100%")) +
scale_x_continuous(limits = c(100,700), expand = c(0,0),
breaks = c(200,300,400,500,600)) +
geom_text(data = top_peaks_apo_4,
aes(x = fragmz, y = int + 0.05, label = round(fragmz, 2)), hjust = 0.5) +
coord_cartesian(clip = "off") +
labs(x = "Fragment m/z",
y = "Relative Intensity",
#color = "",
title = bquote(bold(.("[C"))[bold(.("33"))]*bold(.("H"))[bold(.("43"))]*bold(.("O"))[bold(.("6"))]*bold(.("N"))[bold(.("4"))]*bold(.("S]"))^bold(.("+"))),
subtitle = paste0("Precursor m/z = ",round(min(apo_rams_4$premz),3), ", RT = ",round(min(apo_rams_4$rt),2),"-", round(max(apo_rams_4$rt),2)," min")) +
theme_classic2(base_size = 12) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.subtitle = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"))
mz_ms2_5 = 698.21969
mz_rt_5 = 27.19
apo_ms2_5 = 637.30554
apo_rt_5 = 27.36
mz_rams_5 = ESIrams_FT$MS2[premz%between%pmppm(mz_ms2_5,100) & rt %between% c(mz_rt_5 - 2, mz_rt_5 + 2)] %>% arrange(int)
mz_rams_5$int = mz_rams_5$int/max(mz_rams_5$int)
top_peaks_mz_5 <- mz_rams_5 %>%
top_n(10, int) %>%
arrange(desc(int))
k = ggplot(mz_rams_5) +
geom_segment(aes(x=fragmz, xend=fragmz, y=int, yend=0), #color = as.character(round(rt, 2))),
) +
scale_y_continuous(limits = c(0,1.075),
expand = c(0,0),
breaks = c(0, .25, .5, .75, 1),
labels = c("0%", "25%", "50%", "75%", "100%")) +
scale_x_continuous(limits = c(100,700), expand = c(0,0),
breaks = c(200,300,400,500,600)) +
geom_text(data = top_peaks_mz_5[c(1,4),],
aes(x = fragmz, y = int + 0.05, label = round(fragmz, 2)), hjust = 0.5) +
geom_text(data = top_peaks_mz_5[c(2),],
aes(x = fragmz, y = int + 0.05, label = round(fragmz, 2)), hjust = 0.85) +
geom_text(data = top_peaks_mz_5[c(3),],
aes(x = fragmz, y = int + 0.05, label = round(fragmz, 2)), hjust = 0.4) +
geom_text(data = top_peaks_mz_5[c(5),],
aes(x = fragmz, y = int + 0.05, label = round(fragmz, 2)), hjust = 0.8) +
coord_cartesian(clip = "off") +
labs(x = "Fragment m/z",
y = "Relative Intensity",
#color = "",
title = bquote(bold(.("[C"))[bold(.("34"))]*bold(.("H"))[bold(.("43"))]*bold(.("O"))[bold(.("6"))]*bold(.("N"))[bold(.("4"))]*bold(.("SCu]"))^bold(.("+"))),
subtitle = paste0("Precursor m/z = ",round(min(mz_rams_5$premz),3), ", RT = ",round(min(mz_rams_5$rt),2)," min")) +
theme_classic2(base_size = 12) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.subtitle = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"))
apo_rams_5 = ESIrams_FT$MS2[premz%between%pmppm(apo_ms2_5,100)] %>% arrange(int)
apo_rams_5$int = apo_rams_5$int/max(apo_rams_5$int)
top_peaks_apo_5 <- apo_rams_5 %>%
top_n(5, int) %>%
arrange(desc(int))
l = ggplot(apo_rams_5) +
geom_segment(aes(x=fragmz, xend=fragmz, y=int, yend=0), #color = as.character(round(rt, 2))),
) +
scale_y_continuous(limits = c(0,1.075),
expand = c(0,0),
breaks = c(0, .25, .5, .75, 1),
labels = c("0%", "25%", "50%", "75%", "100%")) +
scale_x_continuous(limits = c(100,700), expand = c(0,0),
breaks = c(200,300,400,500,600)) +
geom_text(data = top_peaks_apo_5[c(1,3),],
aes(x = fragmz, y = int + 0.05, label = round(fragmz, 2)), hjust = 0.5) +
geom_text(data = top_peaks_apo_5[c(2),],
aes(x = fragmz - 5, y = int + 0.05, label = round(fragmz, 2)), hjust = 1) +
geom_text(data = top_peaks_apo_5[c(4),],
aes(x = fragmz - 5, y = int - 0.035, label = round(fragmz, 2)), hjust = 1) +
geom_text(data = top_peaks_apo_5[c(5),],
aes(x = fragmz + 5, y = int + 0.05, label = round(fragmz, 2)), hjust = 0) +
coord_cartesian(clip = "off") +
labs(x = "Fragment m/z",
y = "Relative Intensity",
#color = "",
title = bquote(bold(.("[C"))[bold(.("34"))]*bold(.("H"))[bold(.("45"))]*bold(.("O"))[bold(.("6"))]*bold(.("N"))[bold(.("4"))]*bold(.("S]"))^bold(.("+"))),
subtitle = paste0("Precursor m/z = ",round(min(apo_rams_5$premz),3), ", RT = ",round(min(apo_rams_5$rt),2)," min")) +
theme_classic2(base_size = 12) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.subtitle = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"))
mz_ms2_6 = 664.19569
mz_rt_6 = 26.2
apo_ms2_6 = 603.28137
apo_rt_6 = 21.3
mz_rams_6 = ESIrams_FT$MS2[premz%between%pmppm(mz_ms2_6,75) & rt %between% c(mz_rt_6-2, mz_rt_6+2)] %>% arrange(int)
mz_rams_6$int = mz_rams_6$int/max(mz_rams_6$int)
top_peaks_mz_6 <- mz_rams_6 %>%
top_n(5, int) %>%
arrange(desc(int))
g = ggplot(mz_rams_6) +
geom_segment(aes(x=fragmz, xend=fragmz, y=int, yend=0), #color = as.character(round(rt, 2))),
) +
scale_y_continuous(limits = c(0,1.075),
expand = c(0,0),
breaks = c(0, .25, .5, .75, 1),
labels = c("0%", "25%", "50%", "75%", "100%")) +
scale_x_continuous(limits = c(100,700), expand = c(0,0),
breaks = c(200,300,400,500,600)) +
geom_text(data = top_peaks_mz_6[c(1,2),],
aes(x = fragmz, y = int + 0.05, label = round(fragmz, 2)), hjust = 0.5) +
geom_text(data = top_peaks_mz_6[c(3),],
aes(x = fragmz, y = int + 0.05, label = round(fragmz, 2)), hjust = 0.8) +
geom_text(data = top_peaks_mz_6[c(4),],
aes(x = fragmz + 2.5, y = int + 0.05, label = round(fragmz, 2)), hjust = 0) +
geom_text(data = top_peaks_mz_6[c(5),],
aes(x = fragmz, y = int + 0.05, label = round(fragmz, 2)), hjust = 0.8) +
coord_cartesian(clip = "off") +
labs(x = "Fragment m/z",
y = "Relative Intensity",
#color = "",
title = bquote(bold(.("[C"))[bold(.("33"))]*bold(.("H"))[bold(.("37"))]*bold(.("O"))[bold(.("7"))]*bold(.("N"))[bold(.("4"))]*bold(.("Cu]"))^bold(.("+"))),
subtitle = paste0("Precursor m/z = ",round(min(mz_rams_6$premz),3), ", RT = ",round(min(mz_rams_6$rt),2)," min")) +
theme_classic2(base_size = 12) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.subtitle = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"))
h = ggplot(mz_rams_1) +
geom_segment(aes(x=fragmz, xend=fragmz, y=int, yend=0), alpha = 0) +
scale_y_continuous(limits = c(0,1.075),
expand = c(0,0),
breaks = c(0, .25, .5, .75, 1),
labels = c("0%", "25%", "50%", "75%", "100%")) +
coord_cartesian(clip = "off") +
labs(x = "Fragment m/z",
y = "Relative Intensity",
#color = "",
title = bquote(bold(.("[C"))[bold(.("33"))]*bold(.("H"))[bold(.("39"))]*bold(.("O"))[bold(.("7"))]*bold(.("N"))[bold(.("4"))]*bold(.("]"))^bold(.("+"))),
subtitle = paste0("No MS2 spectra collected")) +
theme_classic2(base_size = 12) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.subtitle = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"))
space = NULL
plot_grid(a + theme(axis.title.x = element_blank(),
plot.margin = unit(c(0.1, 0.1, 0.1, 0.1), "cm")),
b + theme(axis.title.y.left = element_blank(),
plot.margin = unit(c(0.1, 0.1, 0.1, 0.1), "cm"),
axis.title.x = element_blank()),
c + theme(axis.title.x = element_blank(),
plot.margin = unit(c(0.1, 0.1, 0.1, 0.1), "cm")),
d + theme(axis.title.y.left = element_blank(),
axis.title.x = element_blank(),
plot.margin = unit(c(0.1, 0.1, 0.1, 0.1), "cm")),
e + theme(axis.title.x = element_blank()),
f + theme(axis.title.y.left = element_blank(),
axis.title.x = element_blank(),
plot.margin = unit(c(0.1, 0.1, 0.1, 0.1), "cm")),
g + theme(axis.title.x = element_blank(),
plot.margin = unit(c(0.1, 0.1, 0.1, 0.1), "cm")),
h + theme(axis.title.y.left = element_blank(),
axis.title.x = element_blank(),
plot.margin = unit(c(0.1, 0.1, 0.1, 0.1), "cm")),
i + theme(axis.title.x = element_blank(),
plot.margin = unit(c(0.1, 0.1, 0.1, 0.1), "cm")),
j + theme(axis.title.y.left = element_blank(),
axis.title.x = element_blank(),
plot.margin = unit(c(0.1, 0.1, 0.1, 0.1), "cm")),
k + theme(plot.margin = unit(c(0.1, 0.1, 0.1, 0.1), "cm")),
l + theme(axis.title.y.left = element_blank(),
plot.margin = unit(c(0.1, 0.1, 0.1, 0.1), "cm")),
align = "v",
label_size = 16,
labels = c("(A)","(B)","(C)","(D)","(E)","(F)","(G)","(H)","(I)","(J)","(K)","(L)"),
ncol = 2)
Figure S7
Extracted ion chromatogram (EIC) of Cu ligand (m/z = 654.24751) with multiple peaks.
ESI_63Cu = ESIrams$MS1[mz%between%pmppm(654.24755, ppm = 5)]
ESI_63Cu = data.table(rt = ESI_63Cu$rt, mz = ESI_63Cu$mz, int = ESI_63Cu$int, filename = ESI_63Cu$filename, CuIsotope = "63Cu")
ESI_65Cu = ESIrams$MS1[mz%between%pmppm(656.24574, ppm = 5)]
ESI_65Cu = data.table(rt = ESI_65Cu$rt, mz = ESI_65Cu$mz, int = ESI_65Cu$int * 2.2, filename = ESI_65Cu$filename, CuIsotope = "65Cu")
combined_table <- rbind(ESI_63Cu, ESI_65Cu)
ggplot(combined_table) +
geom_line(aes(x = rt, y = int, color = CuIsotope),
size = 1.25) +
xlim(c(24,30)) +
scale_y_continuous(breaks = c(0.5e6,1e6,1.5e6,2e6,2.5e6,3e6,3.5e6),
labels = scales::scientific) +
labs(x = "Retention Time (min)",
y = "LC-ESI-MS Intensity",
color = "") +
scale_color_manual(values = c("#0A9F9D", "#E54E21"), labels = c(expression(bold(paste(""^"63","Cu"))),
expression(bold(paste(""^"65","Cu × 2.2"))))) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 18) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.title = element_text(face = "bold"),
legend.text = element_text(face = "bold"),
legend.text.align = 0)
Figure S8
Figure S8 are chemical structures generated with ChemDraw.
Figure S9
Voltammograms from Cu ligand analyses with CLE-ACSV
g1 = ggplot(data = MN19 %>% filter(treatment != "SPIKE")) +
aes(x = potential, y = Y, color = treatment) +
geom_line(size = 1) +
scale_color_manual(values = stepped3(16)[16:2], labels = c("0 nM",
"0 nM",
"1 nM",
"2 nM",
"3 nM",
"5 nM",
"7.5 nM",
"10 nM",
"15 nM",
"20 nM",
"30 nM",
"40 nM",
"50 nM",
"75 nM",
"100 nM",
"5 nM spike")) +
labs(title = "MN19", x = "Potential (V)", y = "Current (A)", color = "Cu Added") +
scale_x_reverse() +
guides(color=guide_legend(ncol=4)) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 19) +
theme(legend.position=c(1,0.995),
legend.justification = c("right","top"),
axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.title = element_text(face = "bold", size = 13.5),
legend.text = element_text(face = "bold", size = 13.5))
g2 = ggplot(data = MN20 %>% filter(treatment != "SPIKE")) +
aes(x = potential, y = Y, color = treatment) +
geom_line(size = 1) +
scale_color_manual(values = stepped3(16)[16:2], labels = c("0 nM",
"0 nM",
"1 nM",
"2 nM",
"3 nM",
"5 nM",
"7.5 nM",
"10 nM",
"15 nM",
"20 nM",
"30 nM",
"40 nM",
"50 nM",
"75 nM",
"100 nM",
"5 nM spike")) +
labs(title = "MN20", x = "Potential (V)", y = "Current (A)", color = "Cu Added") +
scale_x_reverse() +
guides(color=guide_legend(ncol=4)) +
coord_cartesian(expand = FALSE) +
theme_classic(base_size = 19) +
theme(legend.position=c(1,0.995),
legend.justification = c("right","top"),
axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(face = "bold", color = "black"),
axis.text.y.left = element_text(face = "bold", color = "black"),
legend.title = element_text(face = "bold", size = 13.5),
legend.text = element_text(face = "bold", size = 13.5))
Cu_CSV_plot = plot_grid(g1 + theme(legend.title = element_text(size = 13.5),
legend.text = element_text(size = 13.5)),
g2 + theme(legend.title = element_text(size = 13.5),
legend.text = element_text(size = 13.5)),
nrow = 2,
labels = c("(A)","(B)"),
align = "hv",
label_size = 23)
Cu_CSV_plot
Figure S10
LC-ICP-MS Standard Curve
std_g1 = ggplot(std_curve) +
aes(x = Concentration,
y = Peak_Area) +
geom_smooth(method = "lm", color = "black", linetype = "dashed") +
geom_point(size = 4, color = "darkorange3") +
labs(x = "Concentration (nM)",
y = "Peak Area (A.U.)") +
scale_x_continuous(expand = c(0.02, 0)) +
scale_y_continuous(expand = c(0.02, 0),
breaks = c(2E5,4E5,6E5,8E5,1E6,1.2E6,1.4E6),
labels = scales::scientific) +
stat_poly_eq(aes(label = paste0("atop(", ..eq.label.., ",", ..rr.label.., ")")),
parse = TRUE,
size = 6.25,
hjust = 0,
label.x = "left",
label.y = "top",
coef.digits = 3,
rr.digits = 3) +
theme_classic2(base_size = 18) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
axis.text.x = element_text(face = "bold"),
axis.text.y.left = element_text(face = "bold"))
std_g2 = ggplot(std_icpms_data) +
aes(x = time/60, y = intensity, color = sampleID) +
geom_line(size = 1.25) +
scale_x_continuous(limits = c(16,18)) +
scale_y_continuous(expand = c(0,0), breaks = c(0,1E4,2E4,3E4,4E4,5E4,6E4,7E4,8E4,9E4)) +
scale_color_manual(values = c("#000000", "#0A9F9D", "#E54E21", "#6C8645", "#D8B70A")) +
labs(x = "Retention Time (min)",
y = "Intensity (cps)",
color = "Ferrioxamine E\nConcentration (nM)") +
theme_classic2(base_size = 18) +
theme(axis.title.x = element_text(face = "bold"),
axis.title.y.left = element_text(face = "bold"),
axis.text.x = element_text(face = "bold"),
axis.text.y.left = element_text(face = "bold"),
legend.title = element_text(face = "bold"), # Bold legend title
legend.text = element_text(face = "bold"),
legend.justification = c(0, 1),
legend.position = c(0.5,0.875))
std_curve_plot = plot_grid(std_g2, NULL, std_g1,
align = "hv",
label_size = 22.5,
labels = c("(A)","","(B)"),
rel_widths = c(1,0.05,1),
hjust = 0,
ncol = 3)
std_curve_plot